Claims:1. A system (100), comprising:
one or more hardware processors (102);
one or more communication interfaces (103); and
one or more memory modules (101) storing a plurality of instructions, the plurality of instructions when executed cause the one or more hardware processors to:
collect (202) real-time data pertaining to working of a machine in an industry environment;
detect (204) an upcoming state transition of the machine, based on the collected data;
determine (206) based on one or more real-time sensor inputs, whether the detected state transition of the machine matches at least one trigger event; and
raise (208) an alert if the detected upcoming state transition of the machine is identified as matching the at least one trigger event.
2. The system (100) as claimed in claim 1, wherein the system (100) detects the upcoming state transition based on a sequence of actions corresponding to a present state of the machine.
3. The system (100) as claimed in claim 2, wherein the sequence of actions is pre-configured.
4. The system (100) as claimed in claim 3, wherein the at least one trigger event is a deviation from the sequence of actions.
5. The system (100) as claimed in claim 1, wherein the at least one trigger event is a threat to health of a user of the machine, further wherein the system (100) identifies the threat to the health of the user by processing at least one real-time sensor input indicating proximity of the user to at least one moving component of the machine.
6. The system (100) as claimed in claim 1, wherein the system collects information pertaining to a user action in response to the alert raised by the system as a feedback.
7. A processor-implemented method (200), comprising:
collecting (202), via one or more hardware processors (102), real-time data pertaining to working of a machine in an industry environment;
detecting (204), via the one or more hardware processors (102), an upcoming state transition of the machine, based on the collected data;
determining (206), via the one or more hardware processors (102), based on one or more real-time sensor inputs, whether the detected state transition of the machine matches at least one trigger event; and
raising (208) an alert, via the one or more hardware processors (102), if the detected upcoming state transition of the machine is identified as matching the at least one trigger event.
8. The method as claimed in claim 7, wherein the upcoming state transition is detected based on a sequence of actions corresponding to a present state of the machine.
9. The method as claimed in claim 8, wherein the sequence of actions is pre-configured.
10. The method as claimed in claim 9, wherein the at least one trigger event is a deviation from the sequence of actions.
11. The method as claimed in claim 7, wherein the at least one trigger event is a threat to health of a user of the machine, further wherein the threat to the health of the user is identified by processing at least one real-time sensor input indicating proximity of the user to at least one moving component of the machine.
12. The method as claimed in claim 7, wherein information pertaining to a user action in response to the alert raised is collected as a feedback.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

Method and system for safety monitoring in an industrial environment

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.


TECHNICAL FIELD
The disclosure herein generally relates to an industrial environment, and, more particularly, to safety monitoring in an industrial environment.

BACKGROUND
Various types of machines are being used in different industries to handle various functions. With most of the industries promoting industry automation, extent of usage of the machines are increasing. In some industries complete automation may not be possible as the machines may be required to be supervised and user interventions/inputs maybe required at some stages of processing (i.e. semi-automatic).
Typically a sequence of operations/actions to be performed by such machines may be pre-defined, and the machines perform actions based on the defined sequence of actions. A user/operator who supervises working of a machine need to be knowledgeable regarding working of the machine and various state transitions of the machine (i.e. transition from one state to another/ switching from one operation to another). However as the operators may not be properly trained, their knowledge varies from one person to another. If any of the operators accidentally deviates from the pre-defined sequence of actions, it may affect optimum performance of the machine. Also, proximity of the operator to the machine may pose health threat to the operator.
SUMMARY
Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a processor-implemented method for safety monitoring in an industry environment is provided. In this method, initially real-time sensor data pertaining to working of a machine in an industry environment is collected, via one or more hardware processors. Further based on the collected sensor data, an upcoming state transition of the machine is detected, via the one or more hardware processors. Further, based on one or more real-time sensor inputs, whether the detected state transition of the machine matches at least one trigger event is determined via the one or more hardware processors. If the detected upcoming state transition of the machine is identified as matching the at least one trigger event, then an alert is raised via the one or more hardware processors.
In another aspect, a system for safety monitoring in an industry environment is provided. The system comprises one or more hardware processors, one or more communication interfaces, and one or more memory modules storing a plurality of instructions. The plurality of instructions when executed cause the one or more hardware processors to initially collect real-time sensor data pertaining to working of a machine in an industry environment. Further based on the collected sensor data, an upcoming state transition of the machine is detected. Further, based on one or more real-time sensor inputs, the system determines whether the detected state transition of the machine matches at least one trigger event and if the detected upcoming state transition of the machine is identified as matching the at least one trigger event, the system raises an alert.
In yet another aspect, a non-transitory computer readable medium for safety monitoring in an industry environment is provided. The non-transitory computer readable medium performs the safety monitoring by executing the following method: Initially real-time sensor data pertaining to working of a machine in an industry environment is collected, via one or more hardware processors. Further based on the collected sensor data, an upcoming state transition of the machine is detected, via the one or more hardware processors. Further, based on one or more real-time sensor inputs, whether the detected state transition of the machine matches at least one trigger event is determined via the one or more hardware processors. If the detected upcoming state transition of the machine is identified as matching the at least one trigger event, then an alert is raised via the one or more hardware processors.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
FIG. 1 illustrates an exemplary system for safety monitoring in an industry environment, according to some embodiments of the present disclosure.
FIG. 2 is a flow diagram depicting steps involved in the process of safety monitoring in the industry environment, by the system of FIG. 1, according to some embodiments of the present disclosure.
FIG. 3 is a flow diagram depicting steps involved in the process of identifying a trigger event, by the system of FIG. 1, according to some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
Referring now to the drawings, and more particularly to FIG. 1 through FIG. 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 illustrates an exemplary system for safety monitoring in an industry environment, according to some embodiments of the present disclosure. In an embodiment, the system 100 includes one or more hardware processors 102, communication interface(s) or input/output (I/O) interface(s) 103, and one or more data storage devices or memory modules 101 operatively coupled to the one or more hardware processors 102. The one or more hardware processors 102 that are hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, graphics controllers, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like.
The I/O interface(s) 103 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface(s) can include one or more ports for connecting a number of devices to one another or to another server. The I/O interface(s) 103 further includes a plurality of sensors configured to monitor and collect information pertaining to one or more parameters indicating working of the machine(s) being monitored. The machine being monitored can be any machine such as but not limited to a Computer Numerical Control (CNC) machine, being used in an industrial environment. Further, the term ‘working of the machine’ may also refer to state transitions of the machine. The sensors may also include one or more proximity sensors to monitor proximity of one or more users/operators to the machine(s) being monitored.
The memory module(s) 101 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (not shown) of the system 100 can be stored in the memory module(s) 101.
In an embodiment, the system 100 includes one or more data storage devices or memory module(s) 101 operatively coupled to the one or more hardware processors 102 and is configured to store instructions configured for execution of steps of the method 200 by the one or more hardware processors 102.
The system 100 is configured to collect, using the one or more of the plurality of sensors, data pertaining to working of the machine. Such data may include, but not limited to position of various components of the machine, vibrational parameters, data pertaining to direction and speed of movement of certain components of the machine, and so on. One or more of such data (one at a time or in any suitable combination) represents/indicates a state of the machine. In an embodiment, different states of the machine and corresponding actions are pre-configured, and are represented along with other parameters, as a Markov function:
M= (S,A,?,R,?) --- (1)
Where,
S ? defines set of states
A ? defines set of actions (action state)
?? defines transition probability from one state to another (i.e. from si to si+1)
R ? Reward received in transition of state si to si+1 (which is updated based on feedback pertaining to user action corresponding to a recommendation made by the system 100)
?? forgetting factor/discount factor (used to remove stale data)
Data represented in the form of the Markov function is used by the system 100 as a reference data to determine any trigger event. In addition to the state transitions indicated, the system 100 also possesses information on history of allowed state transitions. The system 100 collects real-time sensor data and processes the collected sensor data to identify a current state (S) of the machine and the corresponding action state (A).
The machine is configured to follow the sequence of actions defined in A. However, in certain scenarios, an authorized user/operator may alter the sequence by placing a sequence change instruction. In an embodiment, the system 100 identifies such sequence change instruction(s) as a trigger event, as it causes the machine to deviate from the pre-defined sequence of actions, and accordingly raises an alarm to inform the user/operator that the machine is deviating from the pre-defined sequence of actions. The user/operator may choose to ignore/turn down the alarm or may act according to the alarm. For example, in a particular scenario the user continues with the change in sequence of actions for a better result/output, despite the alarm being triggered by the system 100. In another example, the deviation from the pre-defined sequence of actions may be unintentional and in that case the user may choose to make the machine follow the pre-defined sequence. Data pertaining to such user actions taken in response to the alarm raised is collected by the system 100 as a feedback.
Further, upon detecting the upcoming state transition (which may be based on the pre-defined sequence of actions or based on a detected sequence change instruction placed by the user), the system 100 checks, based on sensor data, whether the upcoming state transition poses any health threat to the user/operator. For example, consider that the detected upcoming state transition would cause one or more parts/components of the machine to rotate. If the sensor values indicate that the user in close proximity to the machine, i.e. within rotating radius of the one or more rotating components of the machine, that means the detected upcoming state transition poses a health threat to the user. Upon detecting such a health threat, the system 100 raises an alarm to warn the user. The system 100 can be configured to collect user response to this alarm, as a feedback data.
The system 100 is configured to use the feedback data to determine the reward function R. The feedback and corresponding reward function can indicate whether or not the system 100 is to be trained further for better results. For example, consider that the system 100 raises alarm whenever a particular trigger event ‘X’ is detected. Here X may be a change in sequence of actions or may be a condition which the system 100 interprets as posing a health hazard to the user. If the user turns down/rejects/ignores the alarm raised by the system 100 every time, then that would indicate that ‘X’ may be a valid event and that the system 100 need not trigger any alarm when ‘X’ is detected. In this scenario, the system 100 is retrained to consider ‘X’ as a valid state change, so that in future occurrences of ‘X’, the system 100 does not trigger any alarm.
In an embodiment, the system 100 has a data model (which may be generated using appropriate machine learning algorithms and appropriate training data) that allows the system 100 to process the collected inputs and to arrive at conclusions (with respect to trigger events, alarms and so on). While retraining the system 100, this data model is updated accordingly. However, upgrading of the data models may increase overhead of storage space in the memory module(s) 101. In order to address this concern, a Least Recently Used (LRU) algorithm or any such suitable algorithm is used to de-allocate the memory for outdated or stale data models.
In an embodiment, the system 100 can be realized in the form of an Industrial Internet of Things (IIoT) platform, which can also facilitate remote monitoring and control of plant equipment(s) and/or the system 100.
FIG. 2 is a flow diagram depicting steps involved in the process of safety monitoring in the industry environment, by the system of FIG. 1, according to some embodiments of the present disclosure. The system 100 collects (202) real-time data pertaining to working of a machine being monitored, as input. By processing the collected data, the system 100 identifies a current state of the machine, and a corresponding action being performed by the machine. The system 100 further detects/identifies (204), based on a pre-defined sequence of actions and/or based on a sequence change instruction placed by a user, an upcoming state transition of the machine.
The system 100 further identifies/determines (206) whether an upcoming state, as indicated by the detected upcoming state transition, matches any trigger event. If any trigger event is detected, then the system 100 raises an alert to warn the user, and the user may take appropriate action(s) in response to the alarm.
FIG. 3 is a flow diagram depicting steps involved in the process of identifying a trigger event, by the system of FIG. 1, according to some embodiments of the present disclosure. The system 100 is configured to identify/detect one or more trigger events by processing (302) data pertaining to detected upcoming state transition of the machine. In an embodiment the system 100 checks whether the upcoming state transition deviates from a pre-defined sequence of actions. This can be done by comparing an upcoming state and/or action (as indicated by the detected upcoming state transition) with a reference data specifying the pre-defined sequence of actions. Any deviation detected (304) from the pre-defined sequence is identified as a trigger event by the system 100, and accordingly an alarm is raised (308).
The system 100 also checks whether the detected upcoming state transition poses any health threat to a user/operator of the machine being monitored, by processing the collected sensor data and data pertaining to state transitions and corresponding actions. If the system 100 detects (306) that the detected upcoming state transition poses any health threat to a user/operator, then the system 100 considers that as a trigger event, and accordingly raises (308) the alarm. In various embodiments, the alarm is raised in one or more suitable formats such as but not limited to 1. Using a display device associated with (internally or externally) the communication interface(s) 103, 2. By sending a message in an appropriate format (for example, a Short Message Service (SMS), a Multi Media Message Service (MMS), Email, and/or any other telecommunication network or internet based message services), 3. By triggering an alarm sound (for example a beep sound), 4. By printing an alert message through a connected printer, and so on.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
The embodiments of present disclosure herein addresses unresolved problem of safety monitoring in an industrial environment. The embodiment, thus provides a mechanism to perform safety monitoring by checking and verifying upcoming state transitions of a machine being monitored, to detect one or more trigger events. Moreover, the embodiments herein further provides a mechanism to raise alert upon detecting one or more trigger events.
It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs.
The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.