DESC:RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, Integrated Circuit (IC) layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.

FIELD OF DISCLOSURE
[0002] The embodiments of the present disclosure generally relate to a fault detection system. In particular, the present disclosure relates to a fault detection system for predicting device events using an Artificial Intelligence (AI) and Machine Learning (ML) based architecture.

BACKGROUND OF DISCLOSURE
[0003] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[0004] In a digital world, with millions of users across the globe, prediction definitely has a power to drive a future of interaction. Feeding a historical dataset into a system that uses machine learning algorithms to predict outcomes makes prediction possible.
[0005] People interact with a number of different electronic devices on a daily basis. However, users may know about an actual condition of these electronic devices only when these devices stop functioning, or are beyond repair. Ultimately, the users may have to replace these electronic devices because of their malfunctioning. This may also lead to increased operational costs for the users and increase in electronic waste (e-waste) in the environment.
[0006] There is, therefore, a need in the art to provide a method and a system that predicts faults in electronic devices by overcoming the shortcomings of the existing prior arts.

OBJECTS OF THE PRESENT DISCLOSURE
[0007] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0008] An object of the present disclosure is to provide a system and a method for predicting faults in Internet of Things (IoT)-based devices in a network.
[0009] Another object of the present disclosure is to automate and forecast device maintenance based on historical usage data for individual IoT-based device.
[0010] Yet another object of the present disclosure is to improve user experience by dynamically notifying users regarding faulty behaviour of devices.
[0011] Yet another object of the present disclosure is to provide a system and a method that automatically raises a service request to a device’s manufacturer based on historical usage data of the device.
[0012] Yet another object of the present disclosure is to save operational costs for users by extending a device life and reducing electronic waste (e-waste) in an environment.

SUMMARY
[0013] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0014] In an aspect, the present disclosure relates to a system for detecting faults in appliances. The system includes one or more processors and a memory operatively coupled to the one or more processors, where the memory includes processor-executable instructions, which on execution, cause the one or more processors to receive one or more parameters associated with one or more appliances and predict one or more faults in the one or more appliances based on the one or more parameters. Further, the one or more processors are to transmit one or more resolution commands corresponding to the one or more faults to one or more target devices.
[0015] In an embodiment, the one or more parameters may include at least one of: information of power consumption, information of temperature readings, information of vibration and movements, information of current and voltage, information of operating hours, information of usage patterns, information of system logging, information of error codes, diagnostic information, information of sensor calibration, information of software version and firmware version, information of load balancing, information of environmental conditions, information of diagnostic test results, and information of user interaction.
[0016] In an embodiment, the one or more processors may receive the one or more parameters from at least one of an Internet Protocol (IP) device associated with each of the one or more appliances, and a non-IP device associated with each of the one or more appliances, via a gateway.
[0017] In an embodiment, the one or more processors may predict the one or more faults by being configured to receive historical performance data and a manufacturer data sheet corresponding to each of the one or more appliances from a database associated with the system and compare the historical performance data, the manufacturer data sheet, and the one or more parameters to determine that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison
[0018] In an embodiment, the one or more processors may train an Artificial Intelligence (AI) model using the historical performance data, the manufacturer data sheet, and the one or more parameters to predict the one or more faults.
[0019] In an embodiment, the one or more target devices may include at least one of: one or more user equipments associated with one or more users, the gateway, and one or more electronic devices associated with a manufacturer.
[0020] Another aspect, the present disclosure relates to a method for detecting faults in appliances. The method includes receiving, by one or more processors associated with a system, one or more parameters associated with one or more appliances and predicting, by the one or more processors, one or more faults in the one or more appliances based on the one or more parameters for transmitting, by the one or more processors, one or more resolution commands corresponding to the one or more faults to one or more target devices.
[0021] In an embodiment, the method may include receiving, by the one or more processors, the one or more parameters from at least one of an Internet Protocol (IP) device associated with each of the one or more appliances, and a non-IP device associated with each of the one or more appliances, via a gateway
[0022] In an embodiment, for predicting, by the one or more processors, the one or more faults in the one or more appliances based on the one or more parameters, the method may include receiving, by the one or more processors, historical performance data and a manufacturer data sheet corresponding to each of the one or more appliances from a database associated with the system and comparing, by the one or more processors, the historical performance data, the manufacturer data sheet, and the one or more parameters for determining, by the one or more processors, that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
[0023] In an embodiment, the method may include training, by the one or more processors, an Artificial Intelligence (AI) model using the historical performance data, manufacturer data sheet, and the one or more parameters for predicting the one or more faults.
[0024] Yet another aspect, the present disclosure relates to a system for detecting faults in appliances. The system includes one or more processors and a memory operatively coupled to the one or more processors, where the memory includes processor-executable instructions, which on execution, cause the one or more processors to receive one or more parameters of one or more appliances and fetch pre-trained data from a cloud platform to predict one or more faults in the one or more appliances based on the one or more parameters and the pre-trained data. Further, the one or more processors are to transmit one or more resolution commands to the one or more appliances based on the one or more faults.
[0025] In an embodiment, the one or more processors may predict the one or more faults, by being configured to compare the pre-trained data, and the one or more parameters and determine that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
[0026] In an embodiment, the one or more processors may determine the one or more resolution commands in response to the prediction of the one or more faults and transmit the one or more resolution commands to at least one of: the IP device associated with each of the one or more appliances, and the non-IP device associated with each of the one or more appliances.
[0027] In an embodiment, the one or more resolution commands may include at least one of: information for updating configuration of each of the one or more appliances, and information for deactivating each of the one or more appliances.
[0028] Yet another aspect, the present disclosure relates to a method for detecting faults in appliances. The method includes receiving, by one or more processors associated with a system, one or more parameters of one or more appliances and fetching, by the one or more processors, pre-trained data from a cloud platform for predicting, by the one or more processors, one or more faults in the one or more appliances based on the one or more parameters and the pre-trained data. Further, the method includes transmitting, by the one or more processors, one or more resolution commands to the one or more appliances based on the one or more faults.
[0029] In an embodiment, for predicting, by the one or more processors, the one or more faults in the one or more appliances based on the one or more parameters, the method may include comparing, by the one or more processors, the pre-trained data, and the one or more parameters and determining, by the one or more processors, that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
[0030] In an embodiment, for transmitting, by the one or more processors, the one or more resolution commands to the one or more appliances based on the one or more faults, the method may include determining, by the one or more processors, the one or more resolution commands in response to the prediction of the one or more faults and transmitting, by the one or more processors, the one or more resolution commands to at least one of: the IP device associated with each of the one or more appliances, and the non-IP device associated with each of the one or more appliances.
[0031] Yet another aspect, the present disclosure relates to an application configured within a user equipment. The user equipment includes one or more processors and a memory operatively coupled to the one or more processors, wherein the memory comprises processor-executable instructions, which on execution, cause the one or more processors to receive one or more resolution commands corresponding to one or more faults from a system. The one or more processors are communicatively coupled with the system, where the system is configured to receive one or more parameters associated with one or more appliances and predict the one or more faults in the one or more appliances based on the one or more parameters to transmit the one or more resolution commands corresponding to the one or more faults to the user equipment.

BRIEF DESCRIPTION OF DRAWINGS
[0032] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0033] FIG. 1A illustrates an exemplary network architecture (100A) in which or with which a system (108) may be implemented, in accordance with embodiments of the present disclosure.
[0034] FIG. 1B illustrates an example block diagram (100B) of a system (108) for detecting faults in appliances, in accordance with embodiments of the present disclosure.
[0035] FIG. 2 illustrates an exemplary block diagram (200) of the system implemented in an Internet of Things (IoT) cloud platform, in accordance with embodiments of the present disclosure.
[0036] FIG. 3 illustrates a sequence diagram (300) depicting a process of notifying anomalies occurred in the appliances in accordance with embodiments of the present disclosure.
[0037] FIG. 4 illustrates an exemplary block diagram depicting a fault detection framework (400) implemented in the system (108), in accordance with embodiments of the present disclosure.
[0038] FIG. 5 illustrates an example flow chart for implementing a method (500) for detecting fault in an IoT cloud platform, in accordance with embodiments of the present disclosure.
[0039] FIG. 6 illustrates an example flow chart for implementing a method (600) for detecting fault in an IoT gateway, in accordance with embodiments of the present disclosure.
[0040] FIG. 7 illustrates an exemplary computer system (700) in which or with which embodiments of the present disclosure may be implemented.
[0041] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DETAILED DESCRIPTION OF DISCLOSURE
[0042] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0043] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0044] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0045] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0046] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner similar to the term “comprising” as an open transition word—without precluding any additional or other elements.
[0047] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0048] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0049] Certain terms and phrases have been used throughout the disclosure and will have the following meanings in the context of the ongoing disclosure.
[0050] The term “Internet of Things” may refer to a computing environment in which physical objects are embedded with devices which enable the physical objects to achieve greater value and service by exchanging data with other systems and/or other connected devices. Each physical object is uniquely identifiable through its embedded device(s) and is able to interoperate within an Internet infrastructure. The acronym “IoT,” as used herein, means “Internet of Things.”
[0051] The term “real-time” may refer to a level of processing responsiveness that a user or a system sense as sufficiently immediate for a particular process or determination to be made, or that enables a processor to keep up with some external process.
[0052] The term “automatically” may refer to without user intervention.
[0053] The term “long short-term memory machine learning model” may refer to a recurrent neural network algorithm which is capable of handling long-term dependencies to identify patterns in series of events/data points. The acronym “LTSM,” as used herein, means “long short-term memory.”
[0054] The term “scene” may refer to a set of events occurring chronologically in a particular time period.
[0055] The terms “Internet of Things (IoT)-based devices” and “appliances are interchangeably represented throughout the specification.
[0056] The terms “Internet of Things (IoT)-cloud platform” and “cloud platform” are interchangeably represented throughout the specification.
[0057] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1A-7.
[0058] FIG. 1A illustrates an exemplary network architecture (100A) in which or with which embodiments of the present disclosure may be implemented.
[0059] Referring to FIG. 1A, the network architecture (100A) may include one or more computing devices (104-1, 104-2…104-N) associated with one or more users (102-1, 102-2…102-N) deployed in an environment. A person of ordinary skill in the art will understand that one or more users (102-1, 102-2…102-N) may be individually referred to as the user (102) and collectively referred to as the users (102). Further, a person of ordinary skill in the art will understand that one or more computing devices (104-1, 104-2…104-N) may be individually referred to as the computing device (104) and collectively referred to as the computing devices (104).
[0060] In an embodiment, each computing device (104) may interoperate with every other computing devices (104) in the network architecture (100A). Referring to FIG. 1A, in an embodiment, the computing devices (104) may be referred to as smart devices operating in a smart environment, for example, an Internet of Things (IoT) system. In some cases, the computing devices (104) may be IoT devices which may be connected to home appliances like a refrigerator, an air conditioner, a washing machine, etc. In such an embodiment, the computing devices (104) may include, but are not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, smart accessories, tablets, smart television (TV), computers, smart security systems, smart home systems, other devices for monitoring or interacting with or for users (102) and/or places, or any combination thereof. In an embodiment, the computing devices (104) may include one or more of the following components: a sensor, a Radio Frequency Identification (RFID) device, a Global Positioning System (GPS), mechanisms for real-time acquisition of data, passive or interactive interfaces, mechanisms of outputting and/or inputting sound, light, heat, electricity, mechanical force, chemical presence, biological presence, location, time, identity, other information, or any combination thereof.
[0061] A person of ordinary skill in the art will appreciate that the computing devices (104) may include, but not be limited by, intelligent, multi-sensing, network-connected devices, that integrate seamlessly with each other and/or with a central server or a cloud-computing system or any other device that is network-connected.
[0062] A person of ordinary skill in the art will appreciate that the computing devices (104) may be UEs and may not be restricted to the mentioned devices and various other devices may be used.
[0063] Referring to FIG. 1A, the computing devices (104) (e.g., IoT devices) may communicate with a system (108), for example, a fault detection system, through a network (106). In an embodiment, the network (106) may include at least one of a Fourth Generation (4G) network, a Fifth Generation (5G) network, or the like. The network (106) may enable the computing devices (104) to communicate between the computing devices (104) and/or with the system (108). As such, the network (106) may enable the computing devices (104) to communicate with other computing devices (104) via a wired or wireless network. The network (106) may include a wireless card or some other transceiver connection to facilitate this communication. In an exemplary embodiment, the network (106) may incorporate one or more of a plurality of standard or proprietary protocols including, but not limited to, Wireless Fidelity (Wi-Fi), ZigBee, or the like. In another embodiment, the network (106) may be implemented as, or include, any of a variety of different communication technologies such as a Wide Area Network (WAN), a Local Area Network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN), or the like.
[0064] Referring to FIG. 1A, the system (108) may include an Artificial Intelligence (AI) engine in which or with which the embodiments of the present disclosure may be implemented. In particular, the system (108) includes the AI engine to facilitate fault detection in the network architecture (100A) based on monitoring data corresponding to usage of the computing devices (104) by the users (102) over a period of time. The data may be parameters such as, but not limited to information of power consumption, information of temperature readings, information of vibration and movements, information of current and voltage, information of operating hours, information of usage patterns, information of system logging, information of error codes, diagnostic information, information of sensor calibration, information of software version and firmware version, information of load balancing, information of environmental conditions, information of diagnostic test results, and information of user interaction.
[0065] Further, the system (108) may be a server or a gateway. In exemplary embodiments, the system (108) may be implemented in the server or the gateway. In an embodiment, the computing devices (104) may be capable of performing data communications and information sharing with the server through the network (106). In an embodiment, the server may be a centralised server or a cloud-computing system or any device that is network connected.
[0066] In accordance with an embodiment of the present disclosure, the system (108) may capture each event triggered at the computing devices (104) and record each event in a database (e.g., 118) (not shown in FIG. 1). In particular, the parameters may be collected at the database (118) from the computing devices (104) based on the triggered events. In an embodiment, the system (108) may access the parameters from the database (118).
[0067] In an embodiment, the system (108) may extract the recorded data from the database and perform pre-processing on the set of data parameters in one or more batches. In an embodiment, the pre-processing of the set of data parameters may be performed by the AI engine utilising one or more machine learning models such as, but not limited to, Long Short-Term Memory (LTSM) machine learning model. A person of ordinary skill in the art will understand that the LTSM may be referred to as a recurrent neural network algorithm which is capable of handling long-term dependencies to identify patterns in series of events/data points. In an embodiment, this model learning or training happens over a period of time in order to identify user patterns.
[0068] In an embodiment, based on the pre-processing, the AI engine predicts future events such as a sudden spike in energy consumption at the computing devices (104) or the like. In an embodiment, the AI engine determines if a probability of occurrence of the future events exceeds a threshold. In an embodiment, the threshold may be pre-determined by the system (108) based on historical usage data and the set of data parameters of the one or more computing devices (104). In an embodiment, the threshold may be a dynamically varying value determined based on periodic computations based on multiple factors including past event data, recent event data, correlation between the past predicted events and actual events at a given time instance, any outliers in the event data, etc. In other embodiments, the threshold may be manually set by an administrator who may have the access rights to the system (108) to override the machine set values of the threshold.
[0069] Further, in an embodiment, the AI engine may provide one or more recommendations for execution of forecasted events at the one or more computing devices (104) in order to prevent damage to the computing devices (104). The one or more recommendations may include turning off an equipment or changing a configuration of any appliance operatively coupled to the computing devices (104) to avoid further damages. The system (108) may dynamically control the one or more computing devices (104) through the network (106) based on the one or more recommendations provided by the AI engine. In an embodiment, the system (108) may automatically schedule the one or more recommendations for execution at the one or more computing devices (104) in the network (106).
[0070] Although FIG. 1A shows exemplary components of the network architecture (100A), in other embodiments, the network architecture (100A) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1A. Additionally, or alternatively, one or more components of the network architecture (100A) may perform functions described as being performed by one or more other components of the network architecture (100A).
[0071] FIG. 1B illustrates an example block diagram (100B) of a system (108) for detecting faults in appliances, in accordance with embodiments of the present disclosure.
[0072] Referring to FIG. 2, the system (108) may include one or more processor(s) (110). The one or more processor(s) (110) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (110) may be configured to fetch and execute computer-readable instructions stored in a memory (112) of the system (108). The memory (112) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to predict faults in appliances that are in communication with the computing devices (104). The memory (112) may comprise any non-transitory storage device including, for example, a volatile memory such as a Random-Access Memory (RAM), or a non-volatile memory such as an Erasable Programmable Read Only Memory (EPROM), a flash memory, and the like.
[0073] In an embodiment, the system (108) may include an interface(s) (114). The interface(s) (114) may comprise a variety of interfaces, for example, interfaces for data input and output devices (I/O), storage devices, and the like. The interface(s) (114) may facilitate communication through the system (108). The interface(s) (114) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing engine(s) (116), and a database (118). The database (118) may include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) (116). In exemplary embodiments, the processing engine(s) (116) may be interchangeably referred to as an Artificial Intelligence (AI) engine throughout the disclosure to predict the faults in the appliances.
[0074] The processing engine(s) (116) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (116). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (116) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (116) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (116). In such examples, the system (108) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (108) and the processing resource. In other examples, the processing engine(s) (116) may be implemented by electronic circuitry. Further, the processing engine (116) may include a parameter determination module (120), a pre-trained data determination module (122), a fault prediction module (124), a resolution commands determination module (126), and other module(s) (128).
[0075] In an embodiment, when the system (108) is implemented in a cloud platform (e.g., an Internet of Things (IoT)-based cloud platform) for detecting the faults in the IoT-based appliances, the parameter determination module (120) may receive parameters from an an Internet Protocol (IP) device or non-IP device which is associated with the appliances. In an exemplary embodiment, the parameters may include, but not limited to information of power consumption, information of temperature readings, information of vibration and movements, information of current and voltage, information of operating hours, information of usage patterns, information of system logging, information of error codes, diagnostic information, information of sensor calibration, information of software version and firmware version, information of load balancing, information of environmental conditions, information of diagnostic test results, information of user interaction, and the like.
[0076] In an embodiment, the system (108) may receive historical performance data and manufacturer data sheet corresponding to the appliances. Once the system (108) receives the historical performance data, manufacturer data sheet, and the parameters of the appliances, the fault prediction module (124) may compare the historical performance data, manufacturer data sheet, and the parameters to determine whether a fault level of the appliances exceeds a predetermined threshold or not. In an embodiment, the fault prediction module (124) may be configured with an AI model. If the fault level exceeds the predetermined threshold, the fault prediction module (124) may predict a type of faults that occurred in the appliances.
[0077] Once the type of faults is determined, the resolution commands determination module (126) may determine resolution commands corresponding to the type of faults and transmit the resolution commands to target devices. In an embodiment, the target device may be, but not limited to, the computing device (e.g., 104) associated with a user (e.g., 102), a gateway, and an electronic device associated with a manufacturer. In exemplary embodiments, the system (108) may receive the parameters from the non-IP device such as, but not limited to Bluetooth, ZigBee, and the like, via the gateway. In exemplary embodiments, the system (108) may train the AI model using the historical performance data, manufacturer data sheet, and the parameters.
[0078] In an embodiment, the system (108) may be implemented in the gateway for detecting the faults in the IoT-based appliances. In exemplary embodiments, the pre-trained data determination module (122) may fetch pre-trained data from the cloud platform and predict the type of faults in the appliances based on the parameters and the pre-trained data using the prediction module (124). Once the type of faults is predicted, the resolution commands determination module (126) may determine the resolution commands corresponding to the type of faults and transmit the resolution commands to the corresponding appliances. In exemplary embodiments, the resolution commands may include, but not limited to information for updating configuration of the appliances, information for deactivating the appliances. When the system (108) is implemented in the gateway, the gateway may directly transmit the resolution commands to the faulty appliances without transmitting the parameters to the cloud platform. This implementation may be used at a time of unavailability of internet connection and timely fault detection may be enabled.
[0079] In exemplary embodiment, a set of data parameters corresponding to events triggered at one or more devices (e.g., the appliances) in a network are recorded and pre-processed in one or more batches. Further, based on the pre-processed set of data parameters and historical device data, a faulty behaviour of the devices may be predicted. Furthermore, based on the pre-processed set of data parameters, one or more recommendations may be provided for execution at the one or more devices in the network, where the one or more recommendations correspond to avoiding further damages of the devices. The user and the manufacturer associated with the devices may be automatically notified about the faulty behaviour of the devices.
[0080] FIG. 2 illustrates an exemplary block diagram (200) of a system (108) implemented in an Internet of Things (IoT) cloud platform, in accordance with embodiments of the present disclosure.
[0081] In an embodiment, the system (108) may include an IoT gateway (206) communicatively connected to the system (108) implemented in an IoT cloud platform. The IoT gateway (206) may be communicatively connected to devices of the IoT environment. The IoT environment may include wireless devices such as, but not limited to, non-Internet Protocol (IP) devices (202), IP devices (204), and the like. In exemplary embodiments, the non-Internet Protocol (IP) devices (202) may include, but not limited to, Bluetooth Low Energy (BLE) devices, ZigBee devices, and the like. Further, the IoT environment may include wired devices such as, but not limited to, instrumentation and control devices using bus protocol, Supervisory Control and Data Acquisition (SCADA) devices, and the like.
[0082] In an embodiment, the IoT gateway (206) may include a device provisioning engine (206-1), a fault management system (206-2), and a firmware upgrade controller (206-3). In an embodiment, the fault management system (206-2) on the IoT gateway (206) may upload device usage data to the system (108) (i.e., the IoT cloud platform). In an embodiment, the fault management system (206-2) may collect diagnostic recommendations from the system (108).
[0083] Further, the IoT gateway (206) may be connected to the system (108) (i.e., IoT cloud platform) through a cloud Application Programming Interface (API) (208) associated with the system (108). The IoT cloud platform may include a rule engine (210), a firmware upgrading engine (212), an access control engine (214), a device provisioning engine (216), a device context managing engine (218), a notification service engine (220), and a device data analysing engine (222). An event processor (224) may execute the one or more engines for detecting faults in appliances.
[0084] In an embodiment, the event processor (224) may execute the device data analysing engine (222) to analyse the historical event data and the usage pattern data corresponding to the IoT devices (104). In an embodiment, the event processor (224) may execute the device provisioning engine (216) to update smart scenes using a reinforcement learning Machine Learning (ML) technique. In exemplary embodiments, the smart scenes may be a predefined configuration that determine about a response of the IoT devices (104) to specific events. The event processor may use ML techniques through the device provisioning engine (216) to update and enhance the smart scenes. The smart scenes may be stored in a database (226). The smart scenes may be transmitted to the IoT gateway (206), which is to be implemented in the one or more IoT devices (104). The device provision engine (206-1) associated with the IoT gateway (206) may implement the smart scenes in the one or more IoT devices (104).
[0085] In an embodiment, the rule engine (210) may use the event data such as a device context to determine dynamic state of each of the one or more IoT devices (104). In an embodiment, the rule engine (210) may derive automatic rules based on device capabilities of the one or more IoT devices (104). In an embodiment, the rule engine (210) may enable rule triggers for the IoT devices (104) for executing the smart scenes. In an embodiment, based on the event data, a fault detection framework (228) may detect fault(s) associated with the IoT devices (104) and provide recommendations to the IoT gateway (206) for execution at the IoT devices (104) to avoid further damage.
[0086] For a given appliance/machine associated with the IoT devices (104), the user (102) may know the device is malfunctioning only when the device has completely stopped functioning. The device may be beyond repair at such a stage, and eventually, the user may have to replace the device. In accordance with embodiments of the present disclosure, based on the implementation of the fault detection system (108) as depicted in FIGs. 1A, 1B and 2, the user may be notified about a device’s faulty behaviour via a mobile application. In an embodiment, an automatic service request may be sent to the device’s manufacturer regarding the device’s fault state. Therefore, the system (108) may enable timely fault detection, thereby reducing unnecessary operational costs for the user.
[0087] FIG. 3 illustrates a sequence diagram (300) of notifying anomalies occurred in appliances, in accordance with embodiments of the present disclosure.
[0088] Referring to FIG. 3, the network architecture (300) may include devices, applications, and the like. For example, the network architecture (300) comprises a home appliance(s) (302), a connected device(s) (304), a gateway (306), a fault detection framework on an Internet of Things (IoT) cloud (308), a mobile application on a user equipment or a computing device associated with a user(s) (310), and an appliance manufacturer (312).
[0089] Referring to FIG. 3, at step A1, energy consumption data associated with the home appliance(s) (302) may be captured by the connected device(s) (304). In exemplary embodiments, the connected device(s) (304) may include, but not limited to smart plug(s) smart relay(s), and the like. In an embodiment, the connected device(s) (304) may be attached to the home appliance(s) (302). In an embodiment, the home appliance(s) (302) may include, but not be limited to, a washing machine, a refrigerator, an air conditioner, or the like. At step A2, the connected device(s) (304) may provide the energy data to the gateway (306) using any of a wireless medium such as, but not limited to ZigBee, BLE, Wi-Fi, and the like. A person skilled in the art may appreciate that the gateway (306) may be similar to the IoT gateway (206) of FIG. 2 in its functionality.
[0090] Further, at step A3, the gateway (306) may provide real-time energy data associated with the home appliance(s) (302) to a system implemented on the IoT cloud platform (308) (e.g., the system (108)). In particular, the gateway (306) may provide the real-time energy data associated with the home appliance(s) (302) to the fault detection framework on the IoT cloud platform (308). At step A4, based on the received real-time data, the IoT cloud platform (308) may process the data and monitor the data to determine abnormalities in the data. In an embodiment, the IoT cloud platform (308) may provide one or more recommendations to the gateway (306) that triggers deactivation commands or update configurations of the home appliance(s) (302) in order to avoid further damages to the home appliance(s) (302). In an embodiment, the IoT cloud platform (308) may provide the recommendations to a user via a mobile application on a user equipment (310), where the mobile application on the user equipment may automatically execute the one or more recommendations at the home appliance(s) (302) to avoid further damages.
[0091] Referring to FIG. 3, At step A5, the IoT cloud (308), in case of any anomaly, may notify the anomaly associated with the home appliance(s) (302) to the gateway (306). Simultaneously, at step A6, the IoT cloud (308) may automatically notify the anomaly associated with the home appliance(s) (302) to the user via the mobile application on the user equipment (310). Thereafter, at step A7, a notification may also be sent from the mobile application (310) to the appliance manufacturer (312) regarding the anomaly associated with the home appliance(s) (302) to invoke customer support.
[0092] At step A8, the gateway (306), based on the recommendations provided by the IoT cloud (308), may deactivate the home appliance(s) (302) or change a configuration of the home appliance(s) (302) to avoid further damage.
[0093] Thus, the system (108) allows for dynamic control of the device(s) (302) based on the set of energy data and historical data related to usage patterns learned by the IoT cloud (308).
[0094] A person of ordinary skill in the art will appreciate that the architecture (300) may be modular and flexible to accommodate any kind of changes. Although FIG. 3 shows exemplary components of the architecture (300), in other embodiments, the architecture (300) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 3. Additionally, or alternatively, one or more components of the architecture (300) may perform functions described as being performed by one or more other components of the architecture (300).
[0095] FIG. 4 illustrates an exemplary fault detection framework (400) implemented in a system (e.g., 108), in accordance with embodiments of the present disclosure.
[0096] Referring to FIG. 4, an event processor (402) (e.g., 224) may capture real-time event data such as, but not limited to, electrical parameters, temperature data, accelerometer data, and other characteristic data associated with devices and/or home appliances. The event processor (402) may provide the event data to the fault detection framework implemented in the system (108). The fault detection framework may filter and/or pre-process the event data (404). In an embodiment, an Artificial Intelligence (AI) or Machine Learning (ML) model (406) may be implemented in order to detect faulty behaviour of the devices or the home appliances. In such an embodiment, the AI/ML model may use the pre-processed data (404), historical data (414), and a data sheet from a manufacturer (412) associated with the devices or the home appliances to analyse the event data pattern and predict a faulty behaviour. Based on the analysis and prediction, a notification service (408) may be executed in order to notify the user associated with the faulty devices or the home appliances regarding the anomaly. Simultaneously, the manufacturer associated with the faulty devices or the home appliances may be notified. In an embodiment, a Hypertext Transfer Protocol (HTTP) application programming interface (API) may be executed to generate a Service Request (SR) at the manufacturer (410) to invoke customer care/support for the faulty devices or the home appliances.
[0097] A person of ordinary skill in the art will appreciate that these are mere examples, and in no way, limit the scope of the present disclosure.
[0098] FIG. 5 illustrates an example flow chart for implementing a method (500) for detecting fault in an Internet of things (IoT) cloud platform, in accordance with embodiments of the present disclosure.
[0099] Referring to FIG. 5, at 502, the method (500) may include receiving one or more parameters associated with one or more appliances. In an embodiment, the method (500) may include receiving the one or more parameters from an Internet Protocol (IP) device associated with each of the one or more appliances and a non-IP device associated with each of the one or more appliances. In exemplary embodiments, the one or more parameters may include information of power consumption, information of temperature readings, information of vibration and movements, information of current and voltage, information of operating hours, information of usage patterns, information of system logging, information of error codes, diagnostic information, information of sensor calibration, information of software version and firmware version, information of load balancing, information of environmental conditions, information of diagnostic test results, and information of user interaction.
[00100] At 504, the method (500) may include predicting one or more faults in the one or more appliances based on the one or more parameters. In an embodiment, the method (500) may include receiving historical performance data and manufacturer data sheet corresponding to each of the one or more appliances and comparing the historical performance data, manufacturer data sheet, and the one or more parameters for determining that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
[00101] At 506, the method (500) may include transmitting one or more resolution commands corresponding to the one or more faults to one or more target devices.
[00102] FIG. 6 illustrates an example flow chart for implementing a method (600) for detecting fault in an Internet of things (IoT) gateway, in accordance with embodiments of the present disclosure.
[00103] Referring to FIG. 6, at 602, the method (600) may include receiving one or more parameters associated with one or more appliances.
[00104] At 604, the method (600) may include fetching pre-trained data from a cloud platform.
[00105] At 606, the method (600) may include predicting one or more faults in the one or more appliances based on the one or more parameters and the pre-trained data. In an embodiment, the method (600) may include comparing the pre-trained data and the one or more parameters to determine that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
[00106] At 608, the method (600) may include transmitting one or more resolution commands to the one or more appliances based on the one or more faults. In an embodiment, the method (600) may include determining the one or more resolution commands corresponding to the one or more faults and transmitting the one or more resolution commands to the IP device associated with each of the one or more appliances, and the non-IP device associated with each of the one or more appliances.
[00107] FIG. 7 illustrates an exemplary computer system (700) in which or with which embodiments of the present disclosure may be utilized.
[00108] As shown in FIG. 7, the computer system (700) may include an external storage device (710), a bus (720), a main memory (730), a read-only memory (740), a mass storage device (750), communication port(s) (760), and a processor (770). A person skilled in the art will appreciate that the computer system (700) may include more than one processor and communication ports. The processor (770) may include various modules associated with embodiments of the present disclosure. The communication port(s) (760) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100A Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (760) may be chosen depending on a network, such a Local Area Network (LAN), a Wide Area Network (WAN), or any network to which the computer system (700) connects. The main memory (730) may be a random-access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (740) may be any static storage device(s) including, but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (770). The mass storage device (750) may be any current or future mass storage solution, which may be used to store information and/or instructions.
[00109] The bus (720) communicatively couples the processor (770) with the other memory, storage, and communication blocks. The bus (720) can be, e.g., a Peripheral Component Interconnect (PCI)/PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), universal serial bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (870) to the computer system (700).
[00110] Optionally, operator and administrative interfaces, e.g., a display, a keyboard, and a cursor control device, may also be coupled to the bus (720) to support direct operator interaction with the computer system (700). Other operator and administrative interfaces may be provided through network connections connected through the communication port(s) (760). In no way should the aforementioned exemplary computer system (700) limit the scope of the present disclosure. In some embodiments, the user equipment, the system, or any other network entity may be implemented as the computer system (700) to perform the methods discussed herein.
[00111] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.

ADVANTAGES OF THE PRESENT DISCLOSURE
[00112] The present disclosure provides a system and a method for predicting faults in one or more devices in a network.
[00113] The present disclosure provides automation and forecasting of device maintenance based on historical usage data for individual computing device.
[00114] The present disclosure provides an improved user experience by dynamically notifying users regarding faulty behaviour of devices.
[00115] The present disclosure provides a system and a method that automatically raises a service request to a device’s manufacturer based on historical usage data of the device.
[00116] The present disclosure facilitates saving of operational costs for users by extending a device life and reducing electronic waste (e-waste) in an environment.
[00117] The present disclosure facilitates scalability to industrial equipments or the like.
,CLAIMS:1. A system (108) for detecting faults in appliances, comprising:
one or more processors (110); and
a memory (112) operatively coupled to the one or more processors (110), wherein the memory (112) comprises processor-executable instructions, which on execution, cause the one or more processors (110) to:
receive one or more parameters associated with one or more appliances;
predict one or more faults in the one or more appliances based on the one or more parameters; and
transmit one or more resolution commands corresponding to the one or more faults to one or more target devices.
2. The system (108) as claimed in claim 1, wherein the one or more parameters comprise at least one of: information of power consumption, information of temperature readings, information of vibration and movements, information of current and voltage, information of operating hours, information of usage patterns, information of system logging, information of error codes, diagnostic information, information of sensor calibration, information of software version and firmware version, information of load balancing, information of environmental conditions, information of diagnostic test results, and information of user interaction.
3. The system (108) as claimed in claim 1, wherein the one or more processors (110) are to:
receive the one or more parameters from at least one of:
an Internet Protocol (IP) device associated with each of the one or more appliances, and
a non-IP device associated with each of the one or more appliances, via a gateway.
4. The system (108) as claimed in claim 1, wherein the one or more processors (110) are to predict the one or more faults by being configured to:
receive historical performance data and a manufacturer data sheet corresponding to each of the one or more appliances from a database (118) associated with the system (108);
compare the historical performance data, the manufacturer data sheet, and the one or more parameters; and
determine that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
5. The system (108) as claimed in claim 4, wherein the one or more processors (110) are to:
train an Artificial Intelligence (AI) model using the historical performance data, manufacturer data sheet, and the one or more parameters to predict the one or more faults.
6. The system (108) as claimed in claim 3, wherein the one or more target devices comprise at least one of: one or more user equipments associated with one or more users, the gateway, and one or more electronic devices associated with a manufacturer.
7. A method (500) for detecting faults in appliances, comprising:
receiving (502), by one or more processors (110) associated with a system (108), one or more parameters associated with one or more appliances;
predicting (504), by the one or more processors (110), one or more faults in the one or more appliances based on the one or more parameters; and
transmitting (506), by the one or more processors (110), one or more resolution commands corresponding to the one or more faults to one or more target devices.
8. The method (500) as claimed in claim 7, comprising:
receiving, by the one or more processors (110), the one or more parameters from at least one of:
an Internet Protocol (IP) device associated with each of the one or more appliances, and
a non-IP device associated with each of the one or more appliances, via a gateway.
9. The method (500) as claimed in claim 7, wherein predicting (504), by the one or more processors (110), the one or more faults in the one or more appliances based on the one or more parameters comprises:
receiving, by the one or more processors (110), historical performance data and a manufacturer data sheet corresponding to each of the one or more appliances from a database (118) associated with the system (108);
comparing, by the one or more processors (110), the historical performance data, the manufacturer data sheet, and the one or more parameters; and
determining, by the one or more processors (110), that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
10. The method (500) as claimed in claim 9, comprising:
training, by the one or more processors (110), an Artificial Intelligence (AI) model using the historical performance data, manufacturer data sheet, and the one or more parameters for predicting the one or more faults.
11. A system (108) for detecting faults in appliances comprising:
one or more processors (110); and
a memory (112) operatively coupled to the one or more processors (110), wherein the memory (112) comprises processor-executable instructions, which on execution, cause the one or more processors (110) to:
receive one or more parameters of one or more appliances;
fetch pre-trained data from a cloud platform;
predict one or more faults in the one or more appliances based on the one or more parameters and the pre-trained data; and
transmit one or more resolution commands to the one or more appliances based on the one or more faults.

12. The system (108) as claimed in claim 11, wherein the one or more parameters comprise at least one of: information of power consumption, information of temperature readings, information of vibration and movements, information of current and voltage, information of operating hours, information of usage patterns, information of system logging, information of error codes, diagnostic information, information of sensor calibration, information of software version and firmware version, information of load balancing, information of environmental conditions, information of diagnostic test results, and information of user interaction.
13. The system (108) as claimed in claim 11, wherein the one or more processors (110) are to:
receive the one or more parameters from at least one of: an Internet Protocol (IP) device associated with each of the one or more appliances, and a non-IP device associated with each of the one or more appliances.
14. The system (108) as claimed in claim 11, wherein the one or more processors (110) are to predict the one or more faults by being configured to:
compare the pre-trained data, and the one or more parameters; and
determine that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
15. The system (108) as claimed in claim 11, wherein the one or more processors (110) are to:
determine the one or more resolution commands in response to the prediction of the one or more faults; and
transmit the one or more resolution commands to at least one of: the IP device associated with each of the one or more appliances, and the non-IP device associated with each of the one or more appliances.
16. The system (108) as claimed in claim 15, wherein the one or more resolution commands comprise at least one of: information for updating configuration of each of the one or more appliances, and information for deactivating each of the one or more appliances.

17. A method (600) for detecting faults in appliances, comprising:
receiving (602), by one or more processors (110) associated with a system (108), one or more parameters of one or more appliances;
fetching (604), by the one or more processors (110), pre-trained data from a cloud platform;
predicting (606), by the one or more processors (110), one or more faults in the one or more appliances based on the one or more parameters and the pre-trained data; and
transmitting (608), by the one or more processors (110), one or more resolution commands to the one or more appliances based on the one or more faults.
18. The method (600) as claimed in claim 17, comprising:
receiving, by the one or more processors (110), the one or more parameters from at least one of: an Internet Protocol (IP) device associated with each of the one or more appliances, and a non-IP device associated with each of the one or more appliances.
19. The method (600) as claimed in claim 17, wherein predicting (606), by the one or more processors (110), the one or more faults in the one or more appliances based on the one or more parameters comprises:
comparing, by the one or more processors (110), the pre-trained data, and the one or more parameters; and
determining, by the one or more processors (110), that a fault level of each of the one or more appliances exceeds a predetermined threshold based on the comparison.
20. The method (600) as claimed in claim 17, wherein transmitting (608), by the one or more processors (110), the one or more resolution commands to the one or more appliances based on the one or more faults comprises:
determining, by the one or more processors (110), the one or more resolution commands in response to the prediction of the one or more faults; and
transmitting, by the one or more processors (110), the one or more resolution commands to at least one of: the IP device associated with each of the one or more appliances, and the non-IP device associated with each of the one or more appliances.
21. An application configured within a user equipment, wherein the application is configured to:
receive one or more resolution commands corresponding to one or more faults from a system (108),
wherein the application is communicatively coupled with the system (108), and wherein the system (108) is configured to:
receive one or more parameters associated with one or more appliances;
predict the one or more faults in the one or more appliances based on the one or more parameters; and
transmit the one or more resolution commands corresponding to the one or more faults to the user equipment.