Description:TECHNICAL FIELD
[001] This disclosure relates generally to health monitoring of vehicles, and more particularly to a method and a system for monitoring health of an engine mount in a vehicle.

BACKGROUND
[002] In modern vehicles, engine mounts play a crucial role in supporting engine within an engine bay. These elastomeric components are designed to dampen vibrations and minimize the transfer of engine noise and vibrations to a vehicle’s cabin, thereby enhancing comfort and stability while driving.
[003] However, over time, the engine mounts may deteriorate due to wear, aging, or damage, leading to a decline in their effectiveness. Excessive wear or damage to the engine mounts may result in increased vibrations transmitted to the vehicle’s cabin, unwanted loads at mounting locations, and customer discomfort due to abnormal noise and vibration.
[004] Traditionally, monitoring a health of the engine mounts has been challenging, often requiring manual inspection or the installation of additional sensors, which may be costly and time-consuming. Moreover, existing techniques may not provide real-time information related to the condition of engine mounts during vehicle operation.
[005] To address these challenges, there is a need for a more efficient and cost-effective method and system that may be capable of monitoring the health of engine mounts in real-time running conditions without the need of additional sensors.

SUMMARY
[006] In one embodiment, a method for monitoring health of an engine mount in a vehicle is disclosed. The method may include receiving, by a controller, real-time vehicle data from a Controller Area Network (CAN) of the vehicle. The method may further include determining acceleration at one or more points of interest in the vehicle based on the real-time vehicle data using a first Machine Learning (ML) model. The method may further include upon determining the acceleration, determining stiffness deterioration level of the engine mount using a second ML model. It should be noted that determination of the stiffness deterioration level may indicate a real-time health status of the engine mount.
[007] In another embodiment, a system for monitoring health of an engine mount in a vehicle is disclosed. The system may include a controller configured to receive real-time vehicle data from a Controller Area Network (CAN) of the vehicle. Further, the controller may be configured to determine acceleration at one or more points of interest in the vehicle based on the real-time vehicle data using a first Machine Learning (ML) model. Further, the controller may be configured to determine stiffness deterioration level of the engine mount using a second ML model, upon determining the acceleration. It should be noted that determination of the stiffness deterioration level may indicate a real-time health status of the engine mount.
[008] 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
[009] 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.
[010] FIG. 1 is a block diagram of a system for monitoring health of an engine mount in a vehicle, in accordance with an embodiment of the present disclosure.
[011] FIG. 2 illustrates a functional block diagram of various modules within a memory of a health monitoring device configured to monitor health of an engine mount in a vehicle, in accordance with some embodiments of the present disclosure.
[012] FIG. 3 is a flowchart of a method for monitoring health of an engine mount in a vehicle, in accordance with some embodiment of the present disclosure.
[013] FIG. 4 illustrates an exemplary User Interface (UI) of a display unit depicting health status of an engine mount, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[014] The foregoing description has broadly outlined the features and technical advantages of the present disclosure in order that the detailed description of the disclosure that follows may be better understood. Additional features and advantages of the disclosure will be described hereinafter which forms the subject of the claims of the disclosure. It should be appreciated by those skilled in the art that the conception and specific embodiments disclosed may be readily utilized as a basis for modifying other devices, systems, assemblies, and mechanisms for carrying out the same purposes of the present disclosure. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the scope of the disclosure as set forth in the appended claims. The novel features which are believed to be characteristics of the disclosure, to its device or system, together with further objects and advantages will be better understood from the following description when considered in connection with the accompanying figures. It is to be expressly understood, however, that each of the figures is provided for the purpose of illustration and description only and is not intended as a definition of the limits of the present disclosure.
[015] The terms “including”, “comprises”, “comprising”, “comprising of” or any other variations thereof, are intended to cover a non-exclusive inclusions, such that a system or a device that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
[016] Reference will now be made to the exemplary embodiments of the disclosure, as illustrated in the accompanying drawings. Wherever possible, same numerals have been used to refer to the same or like parts. The following paragraphs describe the present disclosure with reference to FIGs. 1-4. It is to be noted that the system may be employed in any vehicle including, but not limited to, a passenger vehicle, a utility vehicle, an ambulance, a commercial vehicle, or any other vehicle with engine mounts.
[017] Stiffness is a critical factor in determining the effectiveness of engine mounts in isolating engine vibrations within the vehicle. Essentially, it governs the resonant frequency of the entire system i.e., the higher the stiffness of the engine mounts, the higher the natural frequency, resulting in reduced isolation capabilities. However, excessive wear of these engine mounts may lead to a cascade of detrimental effects. Vibrations that should be absorbed and dampened by the engine mounts may instead be transmitted directly to the vehicle's cabin, causing discomfort, and reducing the overall driving experience. Additionally, such wear may result in unwanted loads at Body-In-White (BIW) mounting locations, potentially leading to structural issues that may compromise vehicle safety and integrity, possibly resulting in failures or malfunctions. Moreover, during engine startup or acceleration, worn engine mounts may fail to adequately stabilize the engine, resulting in noticeable engine shake and abnormal noise, further exacerbating customer discomfort. Therefore, effective monitoring and maintenance of engine mount stiffness are crucial for ensuring optimal vehicle performance, comfort, and safety. The present disclosure proposes a method and a system for real-time health monitoring of engine mounts in vehicles. This approach facilitates early detection of engine mount stiffness deterioration and allows for timely maintenance or replacement, thereby ensuring optimal vehicle performance and enhancing overall driving experience for vehicle occupants.
[018] Referring now to FIG. 1, a block diagram of a system 100 for monitoring health of an engine mount in a vehicle is illustrated, in accordance with an embodiment of the present disclosure. The system 100 may include a health monitoring device 102 that may be configured to monitor health of the engine mount in the vehicle. More particularly, in order to monitor health of the engine mount, the health monitoring device 102 may determine stiffness deterioration level of the engine mount. As will be appreciated, the more the stiffness deterioration level changes, the poorer may be the health status of the vehicle.
[019] The system 100 may further include one or more inbuilt vehicle sensors 104. The one or more inbuilt vehicle sensors 104 may be an engine knock sensor 104a, an air bag sensor 104b, an engine rpm sensor 104c, an acceleration pedal sensor 104d, a gear position sensor 104e, a vehicle speed sensor 104f, a brake and clutch press sensor 104g, and a gyro sensor 104h. The one or more inbuilt vehicle sensors 104 may be pre-installed within the vehicle to determine real-time vehicle data. The real-time vehicle data may include noise data, acceleration data, engine rpm, engine torque, gear position, vehicle speed, acceleration pedal state, brake pedal state, clutch state, and vehicle gyroscopic data.
[020] By way of an example, the engine knock sensor 104a may be configured to determine noise data from an engine. Further, the air bag sensor 104b may be configured to determine acceleration from an air bag. Further, the engine rpm sensor 104c may be configured to determine engine RPM and engine torque. Further, the acceleration pedal sensor 104d may be configured to determine the acceleration pedal state. Further, the gear position sensor 104e may be configured to determine the gear position. Further, the vehicle speed sensor 104f may be configured to determine the vehicle speed. Further, the brake and clutch press sensor 104g may be configured to determine the brake pedal state and clutch state, Additionally, the gyro sensor 104h may be configured to determine vehicle gyroscopic data in x, y, and z coordinates.
[021] The one or more inbuilt vehicle sensors 104 may be connected with the health monitoring device 102 via a communication link 110 to transfer the real-time vehicle data to the health monitoring device 102. The communication link 110 may be an intra vehicular communication network that may include, but not limited to, CAN (Controlled Area Network), CAN FD (Controlled Area Network Flexible Data-Rate), LIN (Local Interconnect Network), local area network (LAN), wide area network (WAN), Ethernet, and the like.
[022] The health monitoring device 102 may further include a controller 106 and a memory 108. In some embodiments, the controller 106 may be an Electronic Control Unit (ECU) of the vehicle. The memory 108 may store instructions that, when executed by the controller 112, cause the controller 112 to perform health monitoring of the vehicle. The memory 108 may be a non-volatile memory or a volatile memory. Examples of non-volatile memory may include, but are not limited to a flash memory, a Read Only Memory (ROM), a Programmable ROM (PROM), Erasable PROM (EPROM), and Electrically EPROM (EEPROM) memory. Examples of volatile memory may include but are not limited to Dynamic Random Access Memory (DRAM), and Static Random-Access memory (SRAM). The memory 108 may also store various real-time vehicle data (for example, noise data, acceleration data, engine rpm, engine torque, gear position, vehicle speed, acceleration pedal state, brake pedal state, clutch state, and vehicle gyroscopic data, etc.) that may be captured, processed, and/or required by the system 100 using the one or more inbuilt vehicle sensors 104.
[023] As will be described in greater detail in conjunction with FIGS. 2 – 4, in order to monitor health of engine mounts in the vehicle, the controller 106 may initially receive real-time vehicle data from the communication link 110 (e.g., CAN) of the vehicle. Further, the controller 106 may determine acceleration at one or more points of interest in the vehicle based on the real-time vehicle data using a first Machine Learning (ML) model. Further, the controller 106 may determine stiffness deterioration level of the engine mount using a second ML model. The determination of the stiffness deterioration level may indicate a real-time health status of the engine mount.
[024] The system 100 may further include an in-vehicle display unit 112 (e.g., an infotainment unit) having a User Interface (UI). The display unit 112 may be used for displaying the health status of the engine mount to the user via the user interface of in-vehicle display unit 112.
[025] In some embodiments, the stiffness deterioration level determined by the system 100 may undergo comparison with a predefined threshold level. By comparing the stiffness deterioration level to the predefined threshold, the system 100 may effectively identify instances where the deterioration exceeds acceptable limits, indicating a need for immediate attention or maintenance. If the stiffness deterioration level exceeds the predefined threshold level, the system 100 may generate an alert to notify the user. This alert may serve as a proactive measure to draw attention to potentially critical issues with the engine mount, prompting timely action to address any emerging problems and prevent further deterioration.
[026] In some embodiments, the health monitoring device 102 may include various controllers such as, the Engine ECU, the controller 106, an electric power assisted steering (EPAS) controller, an electronic stability control (ESC) controller, etc., which may be configured to monitor and control various components of the vehicle. For example, in an embodiment, the controllers may execute one or more control algorithms to facilitate monitoring and controlling of the components such as, but not limited to, the one or more inbuilt vehicle sensors for determining the speed, torque, braking, etc. In an embodiment, the controllers may include software executable controllers which may be implemented on hardware platform or a hybrid device that combines controller functionality and other functions such as visualization. The control software or algorithms executed by automobile controllers may include algorithm to process input signal read from the vehicle components or industrial devices or sensors, etc.
[027] Referring now to FIG. 2, functional block diagram 200 of various modules within the memory 108 of the health monitoring device 102 is illustrated, in accordance with some embodiments of the present disclosure. The various modules may be an acceleration determination module 204 and a stiffness determination module 206 that may be configured to monitor real-time health of the engine mount in the vehicle.
[028] In order to monitor heath of the engine mount, initially real-time vehicle data 202 may be received from a CAN of the vehicle. The real-time vehicle data 202 may include various data such as, noise data, acceleration data, engine RPM, engine torque, gear position, vehicle speed, acceleration pedal state, brake pedal state, clutch state, and vehicle gyroscopic data that may be obtained from the one or more inbuilt vehicle sensors while operating the vehicle.
[029] One the real-time data 202 is received, the acceleration determination module 204 may determine acceleration at one or more points of interest in the vehicle based on the real-time vehicle data. The one or more points of interest may include each side of engine mount, engine Centre of Gravity (C.G), and steering column of the vehicle. The acceleration determination module 204 may employ a first Machine Learning (ML) model 204a to determine amount of acceleration or vibration level observed at each engine side and vehicle cabin side.
[030] In a more elaborative way, the first ML model 204a may utilize the real-time vehicle data to determine acceleration or vibration observed at each side of the engine mount i.e., engine mount A, positioned at front of an engine bay, for providing stability and support to the engine, particularly during acceleration and deceleration, engine mount B, positioned on one side of the engine bay, and engine mount C positioned on other side of the engine bay, that provide overall structural integrity of the vehicle by absorbing and dampening vibrations generated by the engine’s operation.
[031] Additionally, the first ML model 204a may determine the acceleration or vibration levels experienced at the engine’s C.G. This point may serve as a vital reference for understanding the overall dynamic behavior of the vehicle, as it represents the theoretical balance point of the entire engine assembly. Monitoring the acceleration or vibration at this location provides valuable information related to the overall stability and performance of the vehicle.
[032] Furthermore, the first ML model 204 may determine acceleration or vibration observed at steering column, a critical component of a vehicle steering system. By assessing the acceleration or vibration levels at the steering column, the first ML model 204a acquires valuable information about the dynamic forces acting on the vehicle’s steering mechanism. This data may be essential for ensuring precise steering control and responsiveness, thereby enhancing the overall driving experience and safety.
[033] In order determine the acceleration, the first ML model 204a may employ a neural network algorithm. The neural network algorithm may be trained on a large dataset having real-time vehicle data collected from various inbuilt vehicle sensors. This dataset includes information such as engine RPM, vehicle speed, acceleration pedal state, and other relevant parameters. By analyzing this data, the neural network may learn to effectively correlate input variables with observed acceleration or vibration levels at engine mounts A, B, and C, the engine C.G, and the steering column.
[034] Upon determining the acceleration at various point of interests within the vehicle by the first ML model 204a, the stiffness determination module 206 may determine stiffness deterioration level of the engine mount. The stiffness determination module 206 may employ a second ML model 206a to determine the stiffness deterioration level.
[035] To further elaborate, the second ML model 206a may utilize the determined acceleration data to determine stiffness deterioration level of the engine mount. In order determine the stiffness deterioration level, the second ML model 206a may employ a decision tree regression algorithm. Decision tree regression is a supervised learning algorithm that is well-suited for predicting continuous numerical values, for tasks like determining stiffness deterioration levels. During a training phase, the decision tree algorithm may be provided with a dataset containing examples of real-world observations of stiffness deterioration levels and corresponding input features such as acceleration data, engine RPM, vehicle speed, and other relevant parameters. The algorithm may learn to construct a decision tree based on these input features that accurately predicts the stiffness deterioration level.
[036] Based on training, the second ML model 206a may analyze a relationship between the acceleration and stiffness deterioration to effectively predict stiffness deterioration level experienced by the engine mount. The determination of stiffness deterioration level may be based on logic associated with a lookup table 208.
[037] The lookup table 208 may include predefined threshold values of stiffness change corresponding to different levels of deterioration. Once the second ML model 206a determines a stiffness change value (expressed in percentage) of the engine mount based on the determined acceleration data, this stiffness change value may be mapped with the predefined threshold stiffness values in the lookup table 208. The mapping process may involve identifying the range of stiffness change values that includes the determined stiffness change (in percentage). Based on the mapping in the lookup table, the logic may determine a corresponding deterioration level of the engine mount. This determination may be typically based on a severity or level of stiffness deviation from a nominal or ideal value. For example, smaller stiffness change may correspond to a lower deterioration level, while larger stiffness change may indicate a higher level of deterioration.
[038] The determined deterioration level may then be used to categorize a health status 210 of the engine mount. The health status 210 may be conveyed to the user through a user interface of the in-built vehicle display unit. The health status 210 of the engine mount may be categorized as one of good, moderate, or poor, depending on the level of stiffness change. For instance, if the determined stiffness change falls within a first predefined threshold range, the health status may be categorized as “Good”, indicating satisfactory performance of the engine mount. If the stiffness change exceeds the first predefined range but falls within a second predefined threshold range, the health status may be categorized as “Moderate”, suggesting some deterioration but still acceptable performance. However, if the stiffness change exceeds the second predefined threshold range, the health status may be categorized as “Poor”, indicating significant deterioration and the need for immediate attention or maintenance.
[039] Referring now to FIG. 3, an exemplary UI 300 of a display unit 112 is illustrated, in accordance with some embodiments of the present disclosure. As explained earlier, once the Once the stiffness deterioration level is determined, the health status may be shown to the user via the UI 300 of the display unit 112. The UI 300 may display deterioration level 302 which may be categorized as DL1, DL2, or DL3, indicating a severity of the stiffness deterioration.
[040] Further, the UI 300 may indicate change of stiffness (expressed in %) occurred in the engine mount. For example, in one scenario, % stiffness change 304 may be found to be up to ± 15%. In another scenario, the % stiffness change 304 may be found be in a range of ± 15% ± 20%. In yet another scenario, % stiffness change 304 may be found to be beyond ± 20%.
[041] Further, the UI 300 may indicate health status 306 for each scenario of stiffness change. For example, in the scenario of ± 15% stiffness change, the health status 306 may be depicted as “Good”. In the scenario of ± 15% to ± 20% stiffness change, the health status 306 may be depicted as “Moderate”. Additionally, in the scenario of stiffness change beyond ± 20%, the health status 306 may be depicted as “Poor”.
[042] Referring now to FIG. 4, a flowchart of a method 400 for monitoring health of an engine mount in a vehicle, in accordance with an embodiment of the present disclosure. It should be noted that the steps 402-408 of the method 400 may be performed by the controller 106 of the system 100. At step 402, real-time vehicle data may be received from a CAN of the vehicle. The real-time vehicle data may include noise data, acceleration data, engine RPM, engine torque, gear position, vehicle speed, acceleration pedal state, brake pedal state, clutch state, and vehicle gyroscopic data.
[043] Further, at step 404, acceleration at one or more points of interest in the vehicle may be determined based on the real-time vehicle data using a first ML model. The one or more points of interest may include each side of engine mount, engine C.G, and a steering column of the vehicle. The first ML model may employ a neural network algorithm to determine the acceleration.
[044] Upon determining the acceleration, further, at step 406, stiffness deterioration level of the engine mount may be determined using a second ML model. It should be noted that determination of the stiffness deterioration level may indicate a real-time health status of the engine mount. The second ML model may employ a decision tree regression algorithm to determine the stiffness deterioration level.
[045] Furthermore, at step 408 the real-time health status of the engine mount may be displayed to the user. In some embodiments, the real-time health status may be displayed via a user interface of an in-vehicle display unit.
[046] In some embodiments, the stiffness deterioration level may be compared with a predefined threshold level. Upon comparison, when the stiffness deterioration level exceeds the predefined threshold level, an alert may be generated to the user. The generation of the alert may ensure that the user is promptly informed of the situation, enabling them to take appropriate measures to address the issue and avoid potential consequences such as increased vibrations, noise, or structural concerns.
[047] As will be appreciated by those skilled in the art, the method and system described in the various embodiments discussed above are not routine, or conventional or well understood in the art. The method and system discussed above may be capable of offering several advantages. For example, by utilizing existing inbuilt vehicle sensors and employing ML algorithms, the method and system enable real-time health monitoring of the engine mount during vehicle operation, providing timely information related to possible issues. Further, the method and system utilize existing inbuilt vehicle sensors, eliminating the need for additional hardware installation, thereby reducing costs and complexity associated with traditional monitoring methods. Further, by accurately assessing stiffness deterioration levels and categorizing engine mount health status in real-time, the method and system facilitate proactive maintenance actions, preventing failures and optimizing vehicle performance. Further, timely detection and resolution of engine mount issues contribute to enhanced vehicle safety and passenger comfort by reducing vibrations, noise, and potential structural concerns. Furthermore, the UI of the infotainment unit enables the vehicle users to easily understand the health status of the engine mount and take appropriate actions as needed.
[048] In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed system and method as discussed above are not routine, conventional, or well understood in the art, as the claimed system and method enable the following solutions to the existing problems in conventional technologies. Further, the claimed system and method clearly bring an improvement in the functioning of the system itself as the claimed system and method provide a technical solution to a technical problem.
[049] The specification has described a method and system for monitoring health of the engine mount in the vehicle. 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.
[050] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.
[051] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”
[052] In addition, where features or aspects of the disclosure are described in terms of Markush groups, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.
[053] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.
, Claims:1. A method (400) for monitoring health of an engine mount in a vehicle, the method (400) comprising:
receiving (402), by a controller (106), real-time vehicle data (202) from a Controller (106) Area Network (CAN) of the vehicle;
determining (404), by the controller (106), acceleration at one or more points of interest in the vehicle based on the real-time vehicle data (202) using a first Machine Learning (ML) model; and
upon determining the acceleration, determining (406), by the controller (106), stiffness deterioration level of the engine mount using a second ML model, wherein determination of the stiffness deterioration level indicates a real-time health status (306) of the engine mount.

2. The method (400) as claimed in claim 1, further comprising displaying (408) the real-time health status (306) of the engine mount to the user via a user interface of an in-vehicle display unit.

3. The method (400) as claimed in claim 1, wherein the real-time health status (306) of the engine mount is categorized as one of good, moderate, or poor based on the stiffness deterioration level.

4. The method (400) as claimed in claim 1, wherein the real-time vehicle data (202) comprises noise data, acceleration data, engine RPM, engine torque, gear position, vehicle speed, acceleration pedal state, brake pedal state, clutch state, and vehicle gyroscopic data.

5. The method (400) as claimed in claim 1, wherein the first ML model (204a) employs a neural network algorithm to determine the acceleration.

6. The method (400) as claimed in claim 1, wherein the second ML model (206a) employs a decision tree regression algorithm to determine the stiffness deterioration level.

7. The method (400) as claimed in claim 1, wherein the one or more points of interest comprises each side of engine mount, engine centre of gravity (C.G), and a steering column of the vehicle.

8. The method (400) as claimed in claim 1, further comprising:
comparing the stiffness deterioration level with a predefined threshold level; and
upon comparing, generating an alert to the user when the stiffness deterioration level exceeds the predefined threshold level.

9. A system (100) for monitoring health of an engine mount in a vehicle, the system (100) comprising:
a controller (106) configured to:
receive real-time vehicle data (202) from a Controller (106) Area Network (CAN) of the vehicle;
determine acceleration at one or more points of interest in the vehicle based on the real-time vehicle data (202) using a first Machine Learning (ML) model (204a); and
determine stiffness deterioration level of the engine mount using a second ML model (206a), upon determination of the acceleration, wherein determination of the stiffness deterioration level indicates a real-time health status (306) of the engine mount.

10. The system (100) as claimed in claim 9, wherein the controller (106) is further configured to display the real-time health status (306) of the engine mount to the user via a user interface of an in-vehicle display unit (112), and wherein the real-time health status (306) of the engine mount is categorized as one of good, moderate, or poor based on the stiffness deterioration level.

11. The system (100) as claimed in claim 9, wherein the real-time vehicle data (202) comprises noise data, acceleration data, engine RPM, engine torque, gear position, vehicle speed, acceleration pedal state, brake pedal state, clutch state, and vehicle gyroscopic data.

12. The system (100) as claimed in claim 9, wherein the first ML model (204a) employs a neural network algorithm to determine the acceleration, and wherein the second ML model (206a) employs a decision tree regression algorithm to determine the stiffness deterioration level.

13. The system (100) as claimed in claim 9, wherein the one or more points of interest comprises each side of engine mount, engine centre of gravity (C.G), and a steering column of the vehicle.

14. The system (100) as claimed in claim 9, wherein the controller (106) is further configured to:
compare the stiffness deterioration level with a predefined threshold level; and
upon comparison, generate an alert to the user when the stiffness deterioration level exceeds the predefined threshold level.