A Machine Learning-Based Prediction Method and System for Low Oil Pressure Faults
By training a fault prediction model using machine learning models and using oil pressure data features to predict future faults, the problem of hysteresis detection in existing technologies is solved, enabling early warning and safety prevention.
CN117131957BActive Publication Date: 2026-05-26DONGFENG COMML VEHICLE CO LTD
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Patent Information
- Application Number
- CN202311021743.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-14
- Publication Date
- 2026-05-26
- Estimated Expiration
- 2043-08-14
AI Technical Summary
Technical Problem
Existing technology can only detect faults when the oil pressure is low, which is delayed and cannot predict or warn in advance, posing a safety hazard.
Method used
By using machine learning models and leveraging vehicle oil pressure data features such as vehicle speed, engine speed, and coolant temperature, a fault prediction model is trained to predict potential low oil pressure faults in the near future and to send out real-time warnings.
Benefits of technology
It enables early prediction of low oil pressure faults, promptly reminding users to change the oil and avoiding driving safety issues caused by the fault.
✦ Generated by Eureka AI based on patent content.
Abstract
This invention discloses a machine learning-based method and system for predicting low oil pressure faults, relating to the field of engine oil maintenance. The method includes the following steps: acquiring oil pressure samples within a prediction period, with each sample corresponding to a vehicle; training a machine learning model on all oil pressure samples to obtain a fault prediction model; and calculating the fault prediction result using the oil pressure data of the vehicle under test. This invention can obtain a fault prediction model through machine learning training based on relevant oil pressure data. This model can calculate and push the probability of an oil pressure fault occurring in the future in real time, thereby providing timely warnings to users, reminding them of potential faults, and guiding them to change the oil in advance to avoid driving safety issues caused by oil pressure-related faults.
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