Abstract: ABSTRACT SYSTEMS FOR ADAPTIVE FAULT DETECTION AND PREDICTIVE MAINTENANCE IN A VEHICLE A system (100) is disclosed. The system (100) comprises at least one sensor (102) configured to detect one or more parameters of the vehicle. The system (100) further comprises at least one processor (104) configured to receive the detected one or more parameters and a first set of data; process the received one or more parameters and the first set of data by filtering noise; compute a diagnostics operating value for each parameter using a model; determine an adaptive threshold for each computed parameter; compare a value of each of the detected one or more parameters; update the diagnostics value and the adaptive threshold; and generate a fault alert if the value of each of the detected one or more parameters exceeds the adaptive threshold. The system (100) further comprises a communication module (106) configured to transmit the generated fault alert to an on-board diagnostic interface corrective action. <>
Description:SYSTEMS FOR ADAPTIVE FAULT DETECTION AND PREDICTIVE MAINTENANCE IN A VEHICLE
FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of vehicle diagnostic and fault detection systems and, in particular, relates to a system and a method for adaptive fault detection and predictive maintenance using a recursive learning algorithm that dynamically adjusts fault detection thresholds based on real-time sensor data and historical operating trends.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Mod , Claims:WE CLAIM:
1. A system (100) for adaptive fault detection in a vehicle, comprising:
at least one sensor (102) configured to detect one or more parameters of the vehicle;
at least one processor (104) operationally coupled with the at least one sensor, wherein the at least one processor (104) is configured to:
receive the detected one or more parameters and a first set of data;
process the received one or more parameters and the first set of data by filtering noise;
compute a diagnostics operating value for each parameter from the one or more parameters using a model;
determine an adaptive threshold for each computed parameter based on a recursive learning algorithm;
compare a value of each of the detected one or more parameters to the determined adaptive threshold to identify one or more faults;
update the diagnostics value and the adaptive threshold dynamically in response to changing one or more conditions;
generate a fault alert if the value of each of the detected one or more parameters exceeds the adaptiv
| # | Name | Date |
|---|---|---|
| 1 | 202521017752-STATEMENT OF UNDERTAKING (FORM 3) [28-02-2025(online)].pdf | 2025-02-28 |
| 2 | 202521017752-REQUEST FOR EXAMINATION (FORM-18) [28-02-2025(online)].pdf | 2025-02-28 |
| 3 | 202521017752-REQUEST FOR EARLY PUBLICATION(FORM-9) [28-02-2025(online)].pdf | 2025-02-28 |
| 4 | 202521017752-PROOF OF RIGHT [28-02-2025(online)].pdf | 2025-02-28 |
| 5 | 202521017752-POWER OF AUTHORITY [28-02-2025(online)].pdf | 2025-02-28 |
| 6 | 202521017752-FORM-9 [28-02-2025(online)].pdf | 2025-02-28 |
| 7 | 202521017752-FORM 18 [28-02-2025(online)].pdf | 2025-02-28 |
| 8 | 202521017752-FORM 1 [28-02-2025(online)].pdf | 2025-02-28 |
| 9 | 202521017752-FIGURE OF ABSTRACT [28-02-2025(online)].pdf | 2025-02-28 |
| 10 | 202521017752-DRAWINGS [28-02-2025(online)].pdf | 2025-02-28 |
| 11 | 202521017752-DECLARATION OF INVENTORSHIP (FORM 5) [28-02-2025(online)].pdf | 2025-02-28 |
| 12 | 202521017752-COMPLETE SPECIFICATION [28-02-2025(online)].pdf | 2025-02-28 |
| 13 | Abstract.jpg | 2025-03-08 |