DESC:CROSS-REFERENCE TO RELATED APPLICATIONS
[001] The application claims priority from an Indian Provisional Application Number: 202441045255 filed on 12-06-2024, the complete disclosures of which, are herein incorporated by reference.
BACKGROUND
Technical Field
[002] The present disclosure relates to a vehicle, and more specifically relates to a system for diagnosing at least one component of at least one vehicle and a method for the same.
Description of the Related Art
[003] Diagnostic tests of one or more vehicles play a crucial role in vehicle manufacturing. The diagnostic test of the one or more vehicles is a process of checking one or more components/devices/systems to detect error data.
[004] In a conventional approach, the automobile manufacturers use an onboard diagnostic (OBD) tool to diagnose the vehicle. The OBD tool is physically connected to an OBD port of the one or more vehicles to extract error data and allow the technician to see the observations. Based on the observations displayed by the OBD tool, the technician predicts one or more faults in the one or more components/devices/systems. Later, the technician fixes the one or more faults in the one or more components/devices/systems using his experience.
[005] The conventional approach is not capable of providing suggestions to the technician to determine the one or more faults and the root cause in the one or more components/devices/systems. Moreover, the conventional approach lacks the ability to foresee potential future failures within the components/devices/systems. So, the technician must solve the one or more faults in the one or more components/devices/systems by identifying the one or more faults using the observations of the OBD tool. So that automobile manufacturers must hire experienced technicians to use the conventional approach (OBD tool) to detect errors, which is an expensive, complex, and time-consuming process.
[006] In addition to that, in the conventional approach, error data detection is done with respect to the individual vehicle and the individual technician so that the conventional approach will not allow a particular error detection approach to be exposed to other technicians. The conventional approach doesn’t provide any facility to store the observations so that technicians cannot access/analyze/use historical data in future when the same error occurs in future.
[007] Accordingly, there remains a need for an improved system and method for diagnosing at least one component of at least one vehicle and therefore addressing the aforementioned issues.
SUMMARY
[008] In view of the foregoing, an aspect herein provides a system to diagnose at least one component of at least one vehicle. The system the at least one vehicle, and a processing module. The at least one vehicle includes a sensing module and an interaction module. The processing module includes a data reception and storage module, a label module, a data analysis module, a suggestion module, a predictive fault analysis module, and a rectification module. The data reception and storage module is operable by one or more processors configured to receive and store, in real-time, one or more device parameters associated with the at least one component of the at least one vehicle from the interaction module.
[009] The label module operable by one or more processors and configured to label the one or more device parameters associated with the at least one component of the at least one vehicle. The data analysis module is operable by one or more processors and configured to determine abnormality in one or more labelled device parameters by comparing the one or more labelled device parameters with a threshold value. The data analysis module further generates at least one error report, stored in the data reception and storage module, at a predetermined interval based on the abnormality. The data analysis module analyze the at least one error report to determine at least one faulty component of the at least one vehicle causing the abnormality.
[0010] The suggestion module is operable by one or more processors and configured to provide at least one suggestion to resolve the abnormality of the at least one faulty component of the at least one vehicle. The predictive fault analysis module is operable by one or more processors and configured to predict at least one forthcoming fault based on the at least one error report stored in the data reception and storage module. The rectification module operable by one or more processors and configured to rectify the abnormality of the at least one faulty component by executing the at least one suggestion generated by the suggestion module and the at least one forthcoming fault generated by the predictive fault analysis module.
[0011] In some embodiments, the sensing module, includes at least one sensor and is disposed on the at least one vehicle. The at least one sensor is configured to collect, in real time, the one or more device parameters associated with the at least one component of the at least one vehicle.
[0012] In some embodiments, the interaction module, includes an Internet of Things (IoT) device and disposed on the at least one vehicle, is configured to collect, in real time, the one or more device parameters associated with the at least one component of the at least one vehicle from the sensing module, and to wirelessly transmit the one or more device parameters to the data reception and storage module in real-time.
[0013] In some embodiments, the processing module further includes an annotation module operable by one or more processors and configured to annotate the one or more device parameters associated with the at least one component of the at least one vehicle.
[0014] In some embodiments, the data reception and storage module includes one or more historical device parameters associated with the at least one component of the at least one vehicle.
[0015] In some embodiments, the processing module further includes a notification module operable by one or more processors and configured to generate and transfer one or more notifications to the user of the at least one vehicle via either the user-computing device, or the interaction module. The one or more notifications include one or more alerts related to the abnormality, and levels of abnormality in the one or more device parameters associated with the at least one component of the at least one vehicle, the at least one suggestion generated by the suggestion module, and the at least one forthcoming fault generated by the predictive fault analysis module.
[0016] In another aspect, a method for diagnosing at least one component of at least one vehicle is provided. The method includes (i) receiving, by a data reception and storage module, one or more device parameters associated with the at least one component of the at least one vehicle from the interaction module in real time, (ii) labelling, by a label module, the one or more device parameters associated with the at least one component of the at least one vehicle, (iii) determining, by a data analysis module, abnormality in one or more labelled device parameters by comparing the one or more labelled device parameters with a threshold value, (iv) generating, by the data analysis module, at least one error report, stored in the data reception and storage module, at a predetermined interval based on the abnormality (v) analyzing, by the data analysis module, the at least one error report to determine at least one faulty component of the at least one vehicle causing the abnormality, (vi) providing, by a suggestion module, at least one suggestion to resolve the abnormality of the at least one faulty component of the at least one vehicle, (vii) predicting, by a predictive fault analysis module, at least one forthcoming fault based on the at least one error report stored in the data reception and storage module, (viii) rectifying, by a rectification module, the abnormality of the at least one faulty component by executing the at least one suggestion generated by the suggestion module and the at least one forthcoming fault generated by the predictive fault analysis module.
[0017] In some embodiments, the method further includes the step of: collecting, by a sensing module, the one or more device parameters associated with the at least one component of the at least one vehicle in real time. The sensing module, comprising at least one sensor and disposed on the at least one vehicle.
[0018] In some embodiments, the method further includes the step of: collecting, by the interaction module, the one or more device parameters associated with the at least one component of the at least one vehicle from the sensing module in real time, and wirelessly transmitting, by the interaction module, the one or more device parameters to the data reception and storage module in real-time.
[0019] In some embodiments, the method further includes the step of: annotating, by an annotation module, the one or more device parameters associated with the at least one component of the at least one vehicle. The processing module includes an annotation module.
[0020] In some embodiments, the data reception and storage module include one or more historical device parameters associated with the at least one component of the at least one vehicle.
[0021] In some embodiments, the method further includes the step of: generating and transferring, by a notification module, one or more notifications to the user of the at least one vehicle via either the user-computing device, or the interaction module. The one or more notifications include one or more alerts related to the abnormality, and levels of abnormality in the one or more device parameters associated with the at least one component of the at least one vehicle, the at least one suggestion generated by the suggestion module, and the at least one forthcoming fault generated by the predictive fault analysis module.
[0022] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments.

BRIEF DESCRIPTION OF DRAWINGS
[0023] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0024] FIG. 1A and 1B are block diagrams representing a system to diagnose at least one component of at least one vehicle according to embodiments as disclosed herein;
[0025] FIG. 2 is a block diagram of a processing module in accordance with an embodiment of the present disclosure; and
[0026] FIG. 3A and 3B are flowcharts representing the steps of a method for diagnosing the at least one component of the at least one vehicle in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0027] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0028] Various embodiments are provided to diagnose at least one component of at least one vehicle. More particularly, the system and method include the ability to foresee at least one forthcoming fault and provide at least one suggestion by analyzing one or more real-time and historical device parameters of the at least one component of the at least one vehicle. The system further rectifies the abnormality of the at least one faulty component by executing at least one suggestion and at least one forthcoming fault.
[0029] As mentioned, there is a need for an improved system and method for diagnosing at least one component of at least one vehicle. Referring now to the drawings, and more particularly to FIGS. 1 through 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[0030] FIG. 1A and 1B are block diagrams representing a system 100 to diagnose at least one component of at least one vehicle 102 according to embodiments as disclosed herein. The at least one vehicle 102 includes a sensing module 104 and an interaction module 106. In one embodiment, the at least one vehicle 102 may include, but not limited to, an electric vehicle.
[0031] The system 100 further includes a processing module 108. The processing module 108 may be a server. In one embodiment, the processing module 108 may be disposed on the at least one vehicle 102. The sensing module 104 includes at least one sensor 105 and is disposed on the at least one vehicle 102. In one embodiment, the at least one sensor 105 may include, but not limited to, an RPM sensor, a torque sensor, a gas sensor, an optical sensors, a proximity sensor, a touch sensor, an image sensor, a vehicle speed sensor, an accelerometer sensor, an alcohol sensor, a radiation sensor, a position sensor, a temperature sensor, an Infra-infrared sensor, a camera, a strain gauge, an altimeter sensor, Photoelectric sensor, Thermoelectric sensor, Electrochemical sensor, Electromagnetic sensor, Thermoptic sensor, or any other sensor that detects the object, body, distance, depth, current, voltage, altitude, or temperature. The at least one sensor 105 is configured to collect one or more device parameters associated with the at least one component of the at least one vehicle 102. In one embodiment, the one or more device parameters may include, but not limited to, one or more states of the one or more vehicles, State of Power (SOP), State of Energy (SOE), State of Charge (SOC), State of health (SOH) of the one or more battery packs, discharge rate of the one or more battery packs, duration of one or more previous rides, charging rate of the one or more battery packs, instantaneous voltage of the one or more battery packs, resistance of the one or more battery packs, instantaneous current of the one or more battery packs, temperature of the one or more battery packs, temperature of a motor, speed of the motor, torque of the motor, voltage of the motor, and current of the motor.
[0032] In another embodiment, the one or more states of the one or more vehicles may include, but not limited to, a locked state, an unlocked state, an ignition on state, and an ignition off state. In yet another embodiment, the one or more states of the one or more vehicles are collected from a Body Control Module (BCM), or any other control module that is responsible for the smooth function of the one or more vehicles.
[0033] In one embodiment, the at least one component includes one or more electric and electronic components that are placed on the at least one vehicle 102. In one embodiment, the one or more electric and electronic components may include, but not limited to, a Body Control Module, a tire pressure monitoring device/arrangement, a Battery Management System, a junction box, a load (e.g., motor), one or more battery packs, a DC-to-DC converter, a transmission system, breaking arrangements, a safety monitory & warning system, a load control unit, and a display (E.g., human-machine interface).
[0034] The interaction module 106 is disposed on the at least one vehicle 102. The interaction module 106 includes an Internet of Things (IoT) device. In one embodiment, the IoT device may be a Human Machine Interface (HMI). In another embodiment, the interaction module 106 may include a memory and processor. The interaction module 106 is configured to collect the one or more device parameters associated with at least one component of the at least one vehicle 102 from the sensing module 104 in real-time. The interaction module 106 is further configured to wirelessly transmit the one or more device parameters associated with the at least one component of the at least one vehicle 102 to the data reception and storage module 110 in real-time.
[0035] In one embodiment, the interaction module 106 is further configured to wirelessly transmit the one or more device parameters associated with the at least one component of the at least one vehicle 102 to the processing module 108 in real time. The communication between the processing module 108 and the interaction module 106 may be wireless. In one embodiment, the wireless communication may include, but not limited to, a light fidelity, (Li-Fi) network, a wireless fidelity (Wi-Fi) network, a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the Internet, a coaxial cable network, an infrared (IR) network, a fiber optic network, (RF) network, a local area network (LAN), a radio frequency, Bluetooth, and a combination thereof.
[0036] The processing module 108 includes a data reception and storage module 110, a label module 112, a data analysis module 114, a suggestion module 116, a predictive fault analysis module 118, and a rectification module 120. The data reception and storage module 110 is operable by one or more processors and configured to receive and store the one or more device parameters associated with the at least one component of the at least one vehicle 102 from the interaction module 106 in real-time.
[0037] The data reception and storage module 110 includes one or more historical device parameters associated with the at least one component of the at least one vehicle 102 collected and stored previously.
[0038] The label module 112 is operable by one or more processors and configured to label the one or more device parameters associated with the at least one component of the at least one vehicle 102. As used herein, labelling is defined as a process of identifying raw data (images, text files, videos, audio etc.) and adding one or more meaningful and informative labels to provide context.
[0039] The annotation module 113 is operable by one or more processors and configured to annotate the one or more device parameters associated with the at least one component of the at least one vehicle 102. As used herein, annotation (in machine learning) is defined as a process of labelling data to show the outcome you want your machine learning model to predict.
[0040] The data analysis module 114 is operable by one or more processors and configured to determine abnormality in a labelled and annotated one or more device parameters by comparing the labelled and annotated one or more device parameters with a threshold value. In one embodiment, the abnormality may include one or more levels. The one or more levels of the abnormality occurrence may include, but not limited to, a low-level abnormality, a medium-level abnormality, and a high-level abnormality. The one or more levels of the abnormality occurrence are stored in the data reception and storage module 110. As used herein, the abnormality is data that is unusual, unexpected, or inconsistent with the typical patterns or characteristics of the dataset.
[0041] In one embodiment, the threshold value is a pre-set value. In another embodiment, the threshold value may vary based on the at least one component of the at least one vehicle 102. In yet another embodiment, the threshold value may vary based on one or more system requirements.
[0042] The data analysis module 114 is further configured to generate at least one error report at a predetermined interval based on the abnormality. The at least one error report is stored in the data reception and storage module 110. In one embodiment, the at least one error report includes a description of the error data.
[0043] The data analysis module 114 is further configured to analyze the at least one error report to determine at least one faulty component of the at least one vehicle causing the abnormality.
[0044] The suggestion module 116 is operable by one or more processors and configured to provide at least one suggestion to resolve the abnormality of the at least one faulty component of the at least one vehicle 102. In one embodiment, the at least one suggestion may vary based on the one or more severity levels of the abnormality.
[0045] The predictive fault analysis module 118 operable by one or more processors and configured to predict at least one forthcoming fault using at least one historically generated error report. The at least one historically generated error report is generated and stored in the data reception and storage module 110. In one embodiment, the one or more levels of the abnormality in the at least one vehicle 102 will be monitored by the predictive fault analysis module 118 using the at least one historically generated error report of the at least one vehicle 102. The predictive fault analysis module 118 further analyze the at least one historically generated error report of the at least one vehicle 102 to predict the at least one forthcoming fault.
[0046] The rectification module 120 is operable by one or more processors and configured to rectify the abnormality of the at least one faulty component by executing at least one suggestion generated by the suggestion module 116 and the at least one forthcoming fault generated by the predictive fault analysis module 118. In one embodiment, the rectification module 120 may include a derating module to regulate an output power of the battery pack based on the at least one suggestion and the at least one forthcoming fault to overcome the abnormality in the one or more device parameters of the at least one component of the at least one vehicle 102. In one embodiment, the rectification module 120 rectifies the abnormality of the at least one faulty component by considering the one or more levels of the abnormality. The one or more levels of the abnormality may include, but not limited to, a low-level abnormality, a medium-level abnormality, and a high-level abnormality.
[0047] Furthermore, system 100 comprises a user-computing device 122, which is communicatively connected to the processing module 108 and the at least one vehicle 102. The computing device 122 may include, but not limited to, desktop computers, laptops, tablets, and smartphones. In another embodiment, the user-computing device 122 may be a handheld device and/or a wearable device.
[0048] The user-computing device 122 and the interaction module 106 are configured to allow a user 124 to access the one or more device parameters associated with the at least one component of the at least one vehicle 102, access the labelled and annotated one or more device parameters, access the abnormality in the labelled and annotated one or more device parameters, access the at least one error report of the abnormality of the labelled and annotated one or more device parameters, access the at least one faulty component of the at least one vehicle 102, access the one or more historical device parameters associated with the at least one component of the at least one vehicle 102, view the at least suggestion to the abnormality of the labelled and annotated one or more device parameters, and view the at least one forthcoming fault. Further, the user-computing device 122 allows the user 124 to view one or more rectification measures performed by the rectification module 120 by executing the at least one suggestion from the at least one suggestion generated by the suggestion module 116 and the at least one forthcoming fault generated by the predictive fault analysis module 118. In one embodiment, the user-computing device 122 allows the user 124 to provide one or more commands to solve the abnormality of the one or more device parameters associated with the at least one component of the at least one vehicle 102 based on the at least one suggestion, and the at least one forthcoming fault. In one embodiment, the user 124 may include, but not limited to, a driver, a service person/engineer.
[0049] In addition to that, the system 100 further includes a notification module 126 that is configured to generate and transfer one or more notifications to the user 124 of the at least one vehicle 102 via either the user-computing device 122, or the interaction module 106. In one embodiment, the one or more notifications include one or more alerts related to the abnormality, and levels of abnormality in the one or more device parameters associated with the at least one component of the at least one vehicle 102, the at least one suggestion generated by the suggestion module 116, and the at least one forthcoming fault generated by the predictive fault analysis module 118. Based on the one or more alerts received, the user 124 may provide one or more commands to overcome, stop, or derate the performance of at least one vehicle 102.
[0050] In one aspect of the embodiment, the processing module 108 may be disposed on the at least one vehicle 102 as shown in Figure 1 B.
[0051] Exemplary embodiment of the present disclosure: one or more sensors 102 (voltage sensor, current sensor, or temperature sensor) are placed on the one or more vehicles 102. The one or more sensors 105 collect one or more device parameters (E.g., voltage, current, or temperature data) associated with one or more components (E.g., a battery pack, Battery Management System (BMS), or Motor Control Unit (MCU) of the one or more vehicles 102 and share received voltage, current, or temperature data with a Human Machine Interface device.
[0052] The HMI device 106 further shares the voltage, current, or temperature data associated with the battery pack, the BMS, or the MCU with a server wirelessly. The server 108 includes a data reception and storage module 110, a label module 112, an annotation module 113, a data analysis module 114, a suggestion module 116, and a predictive fault analysis module 118. The data reception and storage module 110 receives and stores the voltage, current, or temperature data associated with the battery pack, the BMS, or the MCU in real-time.
[0053] The data reception and storage module 110 includes historical voltage, current, or temperature data associated with the battery pack, the BMS, or the MCU of the at least one vehicle 102. The label module 112 and the annotation module 113 label and annotate the voltage, current, or temperature data associated with the battery pack, the BMS, or the MCU by labeling and annotating each data point to its respective component. The data analysis module 114 determines abnormality of a labelled and annotated voltage, current, or temperature data by comparing the labelled and annotated voltage, current, or temperature data with a threshold value.
[0054] For example, the battery pack temperature will be increased by around 5°C (threshold value for the battery pack) when 10 watts of energy are consumed per hour under normal conditions, but in reality, the temperature rise is beyond the threshold value (8 °C). Then the data analysis module 114 provides the abnormality occurrence of a labelled and annotated voltage, current, or temperature data as low-level abnormality.
[0055] The data analysis module 114 generates at least one error report in a predetermined interval based on the abnormality in the labelled and annotated voltage, current, or temperature data. The at least one error report is stored in the data reception and storage module 110. The data analysis module 114 analyze the at least one error report to determine at least one faulty component of the at least one vehicle 102 that causes the abnormality occurrence (E.g., 8 °C of sudden temperature rise) of the labelled and annotated voltage, current, or temperature data.
[0056] The data analysis module 114 analyzes all potential scenarios from the at least one error report (to find the at least one faulty component), including excessive power draw by an MCU (Motor Control Unit), the impact of environmental parameters on temperature rise, potential BMS temperature sensing issues, supply interruptions, and the possibility of circuit issues or faulty cells within the battery pack that may lead to excessive current draw. Once the data analysis module 114 finds the faulty component that is the battery pack with the faulty cells, the suggestion module 116 provides at least one suggestion (E.g., switch to lower mode, suggestion to the user to change driver behaviour) to resolve the abnormality occurrence of the battery pack. The at least one suggestion will be varied based on the level of abnormality.
[0057] The predictive fault analysis module 118 predicts the at least one forthcoming fault using at least one historically generated error report and historical abnormality. The at least one historically generated error report is generated and stored in the data reception and storage module 110. The at least one forthcoming fault and solutions will replace the battery pack or replace the faulty cells. The system further includes the rectification module to rectify the abnormality by executing the at least one suggestion, like automatically switching the at least one vehicle to a lower driving mode to solve the abnormality of a respective faulty component. Furthermore, the system 100 considers the at least one forthcoming fault generated by the predictive fault analysis module 118 while performing rectification operation.
[0058] In addition to that, the system 100 includes a mobile device 120 allows the user 124 to access: the voltage, current, or temperature data, the labelled and annotated voltage, current, or temperature data, the abnormality occurrence of the labelled and annotated the voltage, current, or temperature data, the at least one error report, the abnormality in the labelled and annotated voltage, current, or temperature data, at least one faulty component (battery pack, or motor). Further, the user-computing device 122 allows the user to view the at least one suggestion (E.g., switch to eco mode) to the abnormality occurrence of the labelled and annotated voltage, current, or temperature data, and the at least one forthcoming fault.
[0059] The HMI device 106 also act as the user-computing device 122 to allow the user to access: the voltage, current, or temperature data, the labelled and annotated voltage, current, or temperature data, the abnormality occurrence of the labelled and annotated the voltage, current, or temperature data, the at least one error report the abnormality in the labelled and annotated voltage, current, or temperature data, at least one faulty component (battery pack). Furthermore, the HMI device 106 allows the user to view the at least one suggestion (E.g., switch to eco mode) to the abnormality occurrence of the labelled and annotated voltage, current, or temperature data, and the at least one forthcoming fault.
[0060] In one embodiment, the processing module 108 stores, extracts, compresses, unzips, parses, and computes the one or more device parameters associated with the at least one component of the at least one vehicle 102 for diagnosing the at least one component of the at least one vehicle 102.
[0061] FIG. 2 is a block diagram of a processing module 108 in accordance with an embodiment of the present disclosure. The processing module 108 includes processor(s) 206, and a memory 202 coupled to the processor(s) 206. The processor(s) 206, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0062] The memory 202 includes a plurality of modules stored in the form of an executable program that instructs the processor 206 to perform the method steps illustrated in Fig 1. The memory 202 has the plurality of modules. The plurality of modules includes a data reception and storage module 110, a label module 112, an annotation module 113, a data analysis module 114, a suggestion module 116, a predictive fault analysis module 118, and a rectification module 120.
[0063] The data reception and storage module 110 is operable by one or more processors configured to receive and store, in real-time, one or more device parameters associated with the at least one component of the at least one vehicle 102 from the interaction module 106. The data reception and storage module 110 includes one or more historical device parameters associated with the at least one component of the at least one vehicle 102.
[0064] The label module 112 is operable by one or more processors and configured to label the one or more device parameters.
[0065] The annotation module 113 is operable by one or more processors and configured to annotate the one or more device parameters associated with the at least one component of the at least one vehicle 102.
[0066] The data analysis module 114 is operable by one or more processors and configured to determine abnormality in one or more labelled device parameters by comparing the one or more labelled device parameters with a threshold value. The data analysis module 114 is further configured to generate at least one error report, stored in the data reception and storage module 110, at a predetermined interval based on the abnormality. The at least one error report is stored in the data reception and storage module 110. The data analysis module 114 is further configured to analyze the at least one error report to determine at least one faulty component of the at least one vehicle causing the abnormality.
[0067] The suggestion module 116 is operable by one or more processors and configured to provide at least one suggestion to resolve the abnormality of the at least one faulty component of the at least one vehicle.
[0068] The predictive fault analysis module 118 operable by one or more processors and configured to predict at least one forthcoming fault based on the at least one error report stored in the data reception and storage module 110. The at least one historically generated error report is generated and stored in the data reception and storage module 110.
[0069] The rectification module 120 is operable by one or more processors and configured to rectify the abnormality of the at least one faulty component by executing the at least one suggestion generated by the suggestion module 116 and the at least one forthcoming fault generated by the predictive fault analysis module 118.
[0070] Computer memory elements may include any suitable memory device(s) for storing data and executable programs, such as read-only memory, random access memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, hard drive, removable media drive for handling memory cards, and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks or defining abstract data types or low-level hardware contexts. An executable program stored on any of the above-mentioned storage media may be executed by the processor(s) 206.
[0071] FIG. 3A and 3B are flowcharts representing the steps of a method for diagnosing the at least one component of the at least one vehicle in accordance with an embodiment of the present disclosure.
[0072] In step 302, the method 300 includes receiving and storing one or more device parameters associated with the at least one component of the at least one vehicle 102 from a sensing module 104 in real-time. In one specific embodiment of the present disclosure, the one or more device parameters associated with the at least one component of the at least one vehicle 102 are received and stored from the sensing module 104 in real-time by a data reception and storage module 110. In one embodiment, the data reception and storage module 110 includes one or more historical device parameters associated with the at least one component of the at least one vehicle 102.
[0073] In step 304, the method 300 includes labelling the one or more device parameters associated with the at least one component of the at least one vehicle 102. In one specific embodiment of the present disclosure, the one or more device parameters associated with the at least one component of the at least one vehicle 102 are labelled by a label module 112.
[0074] In step 306, the method 300 includes determining abnormality in one or more labelled device parameters by comparing the one or more labelled device parameters with a threshold value. In one specific embodiment of the present disclosure, the abnormality is determined in the one or more labelled device parameters by comparing the one or more labelled device parameters with the threshold value by a data analysis module 114.
[0075] In step 308, the method 300 includes generating at least one error report at a predetermined interval based on the abnormality. In one specific embodiment of the present disclosure, the at least one error report is generated in the predetermined interval based on the abnormality in the labelled one or more device parameters by the data analysis module 114. In one embodiment, the error report is stored in the data reception and storage module 110.
[0076] In step 310, the method 300 includes analyzing the at least one error report to determine at least one faulty component of the at least one vehicle causing the abnormality. In one specific embodiment of the present disclosure, the at least one error report is analyzed to determine the at least one faulty component of the at least one vehicle 102 causing the abnormality by the data analysis module 114.
[0077] In step 312, the method 300 includes providing at least one suggestion to resolve the abnormality of the at least one faulty component of the at least one vehicle. In one specific embodiment of the present disclosure, the at least one suggestion is provided to resolve the abnormality of the at least one faulty component of the at least one vehicle by a suggestion module 116.
[0078] In step 314, the method 300 includes predicting at least one forthcoming fault based on at least one error report stored in the data reception and storage module 110. In one specific embodiment of the present disclosure, the at least one forthcoming fault are predicted based on at least one error report stored in the data reception and storage module 110 by a predictive fault analysis module. In one embodiment, the at least one historically generated error report is generated and stored in the data reception and storage module 110.
[0079] In step 316, the method 300 includes rectifying the abnormality of the at least one faulty component by executing the at least one suggestion generated by the suggestion module 116 and the at least one forthcoming fault generated by the predictive fault analysis module 118. In one embodiment of the present disclosure, the abnormality of the at least one faulty component is rectified by executing the at least one suggestion generated by the suggestion module 116 and the at least one forthcoming fault generated by the predictive fault analysis module 118 using a rectification module 120.
[0080] The method 300 also includes collecting the one or more device parameters associated with the at least one component of the at least one vehicle 102 in real time. In one specific embodiment of the present disclosure, the one or more device parameters associated with the at least one component of the at least one vehicle 102 are collected in real time by a sensing module 104. In one embodiment, the sensing module 104 comprises at least one sensor 105 and is disposed on the at least one vehicle 102.
[0081] The method 300 also includes collecting the one or more device parameters associated with the at least one component of the at least one vehicle 102 from the sensing module 104 in real time, and wirelessly transmitting, by the interaction module 106, the one or more device parameters to the data reception and storage module 110 in real-time. In one specific embodiment of the present disclosure, the one or more device parameters associated with the at least one component of the at least one vehicle 102 are collected from the sensing module 104 in real time, and the one or more device parameters are wirelessly transmitted to the data reception and storage module 110 in real-time by the interaction module 106.
[0082] The method 300 also includes annotating the one or more device parameters associated with the at least one component of the at least one vehicle 102. In one specific embodiment of the present disclosure, the one or more device parameters associated with the at least one component of the at least one vehicle 102 are annotated by an annotation module 113. In one embodiment, the processing module 108 includes an annotation module 113.
[0083] In one embodiment, the data reception and storage module 110 includes one or more historical device parameters associated with the at least one component of the at least one vehicle 102.
[0084] The method 300 further includes the step of: generating and transferring, by a notification module 126, one or more notifications to the user 124 of the at least one vehicle 102 via either the user-computing device 122, or the interaction module 106. The one or more notifications include one or more alerts related to the abnormality, and levels of abnormality in the one or more device parameters associated with the at least one component of the at least one vehicle, the at least one suggestion generated by the suggestion module, and the at least one forthcoming fault generated by the predictive fault analysis module.
[0085] The system 100 includes the ability to foresee the at least one forthcoming fault and provides the at least one suggestion by analyzing one or more real-time and historical device parameters of the at least one component of the at least one vehicle 102. The system further rectifies the abnormality of the at least one faulty component by executing the at least one suggestion generated by the suggestion module 116 and the at least one forthcoming fault generated by the prediction and analysis module 118.
[0086] The system 100 provides a user-friendly diagnostic interface that enables end users 124 to efficiently access and analyze the one or more device parameters. The system 100 presents historical and real-time abnormalities in a structured format, allowing quicker identification and resolution of faults. By maintaining a comprehensive fault history and correlating with real-time data, the system 100 supports effective root cause analysis. Further, the system 100 reduces diagnostic time by automating fault detection and offering guided suggestions, minimizing the need for manual investigation, and enabling faster, more accurate problem-solving.
[0087] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein may be practiced with modification within the spirit and scope of the appended claims.

LIST OF REFERENCE NUMERALS
System 100.
at least one vehicle 102.
Sensing module 104.
At least one sensor 105.
Interaction Module 106.
Processing module 108.
Data reception and storage module 110.
Label module 112.
Annotation module 113.
Data analysis module 114.
Suggestion module 116.
Predictive fault analysis module 118.
Rectification module 120
User-computing device 122.
User 124.
Notification Module 126.
Memory 202.
Bus 204.
Processor 206. ,CLAIMS:CLAIMS
I/We claim:
1. A system (100) to diagnose at least one component of at least one vehicle (102), comprising:
the at least one vehicle (102):
a sensing module (104); and
an interaction module (106); and
a processing module (108) comprises:
a data reception and storage module (110) operable by one or more processors configured to receive and store, in real-time, one or more device parameters associated with the at least one component of the at least one vehicle (102) from the interaction module (106);
a label module (112) operable by one or more processors and configured to label the one or more device parameters associated with the at least one component of the at least one vehicle (102);
a data analysis module (114) operable by one or more processors and configured to:
determine abnormality in one or more labelled device parameters by comparing the one or more labelled device parameters with a threshold value;
generate at least one error report, stored in the data reception and storage module (110), at a predetermined interval based on the abnormality;
analyze the at least one error report to determine at least one faulty component of the at least one vehicle causing the abnormality;
a suggestion module (116) operable by one or more processors and configured to provide at least one suggestion to resolve the abnormality of the at least one faulty component of the at least one vehicle;
a predictive fault analysis module (118) operable by one or more processors and configured to predict at least one forthcoming fault based on the at least one error report stored in the data reception and storage module (110); and
a rectification module (120) operable by one or more processors and configured to rectify the abnormality of the at least one faulty component by executing the at least one suggestion generated by the suggestion module (116) and the at least one forthcoming fault generated by the predictive fault analysis module (118).

2. The system (100) as claimed in claim 1, wherein the sensing module (104), comprises at least one sensor (105) and disposed on the at least one vehicle (102), wherein the at least one sensor (105) is configured to collect, in real time, the one or more device parameters associated with the at least one component of the at least one vehicle (102).

3. The system (100) as claimed in claim 1, wherein the interaction module (106), comprises an Internet of Things (IoT) device and disposed on the at least one vehicle (102), is configured to collect, in real time, the one or more device parameters associated with the at least one component of the at least one vehicle (102) from the sensing module (104), and to wirelessly transmit the one or more device parameters to the data reception and storage module (110) in real-time.

4. The system (100) as claimed in claim 1, wherein the processing module (108) further comprises an annotation module (113) operable by one or more processors and configured to annotate the one or more device parameters associated with the at least one component of the at least one vehicle (102).

5. The system (100) as claimed in claim 1, wherein the data reception and storage module (110) comprises one or more historical device parameters associated with the at least one component of the at least one vehicle (102).

6. The system (100) as claimed in claim 1, wherein the processing module (108) further comprises a notification module (126) operable by one or more processors and configured to generate and transfer one or more notifications to the user (124) of the at least one vehicle (102) via either the user-computing device (122), or the interaction module (106), wherein the one or more notifications include one or more alerts related to the abnormality, and levels of abnormality in the one or more device parameters associated with the at least one component of the at least one vehicle (102), the at least one suggestion generated by the suggestion module (116), and the at least one forthcoming fault generated by the predictive fault analysis module (118).

7. The method (300) for diagnosing at least one component of at least one vehicle (102) comprising:
receiving and storing, by a data reception and storage module (110), one or more device parameters associated with the at least one component of the at least one vehicle (102) from the interaction module (106) in real time;
labelling, by a label module (112), the one or more device parameters associated with the at least one component of the at least one vehicle (102);
determining, by a data analysis module (114), in one or more labelled device parameters by comparing the one or more labelled device parameters with a threshold value;
generating, by the data analysis module (114), at least one error report, stored in the data reception and storage module (110), at a predetermined interval based on the abnormality;
analyzing, by the data analysis module (114), the at least one error report to determine at least one faulty component of the at least one vehicle causing the abnormality;
providing, by a suggestion module (116), at least one suggestion to resolve the abnormality of the at least one faulty component of the at least one vehicle;
predicting, by a predictive fault analysis module (118), at least one forthcoming fault based on the at least one error report stored in the data reception and storage module (110); and
rectifying, by a rectification module (120), the abnormality of the at least one faulty component by executing at least one suggestion generated by the suggestion module (116), and the at least one forthcoming fault generated by the predictive fault analysis module 118.

8. The method (300) as claimed in claim 7, wherein the method (300) further comprises the step of: collecting, by a sensing module (104), the one or more device parameters associated with the at least one component of the at least one vehicle (102) in real time, wherein the sensing module (104), comprising at least one sensor (105) and disposed on the at least one vehicle (102).

9. The method (300) as claimed in claim 7, wherein the method (300) further comprises the step of: collecting, by the interaction module (106), the one or more device parameters associated with the at least one component of the at least one vehicle (102) from the sensing module (104) in real time, and wirelessly transmitting, by the interaction module (106), the one or more device parameters to the data reception and storage module (110) in real-time.

10. The method (300) as claimed in claim 7, wherein the method (300) further comprises the step of: annotating, by an annotation module (113), the one or more device parameters associated with the at least one component of the at least one vehicle (102), wherein the processing module (108) comprises an annotation module (113).

11. The method (300) as claimed in claim 7, wherein the data reception and storage module (110) comprises one or more historical device parameters associated with the at least one component of the at least one vehicle (102).

12. The method (300) as claimed in claim 7, wherein the method (300) further comprises the step of: generating and transferring, by a notification module (126), one or more notifications to the user (124) of the at least one vehicle (102) via either the user-computing device (122), or the interaction module (106), wherein the one or more notifications include one or more alerts related to the abnormality, and levels of abnormality in the one or more device parameters associated with the at least one component of the at least one vehicle (102), the at least one suggestion generated by the suggestion module (116), and the at least one forthcoming fault generated by the predictive fault analysis module (118).