Electric Mechanical Brake Torque Control for Predicted Motor Failure
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Solution Overview
Problem
Existing vehicle brake systems fail to predict motor failures in electric mechanical brakes (EMBs) until performance degradation occurs, leading to safety risks due to continuous normal operation despite impending failure.
Innovation Solution
A brake system with a sensor module and controller that uses a motor current sensor and force sensor to detect motor states, employing a machine learning model to predict motor failures based on current signals, and adjusts torque control accordingly to prevent safety hazards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the EMB continues normal operation until failure occurs, then the device complexity is reduced, but the reliability of the brake system deteriorates
Solution Approach 1:
The system performs preliminary failure prediction by analyzing motor current signals before actual failure occurs. The controller continuously monitors current signals and uses machine learning models to predict potential motor failures, enabling preventive maintenance and avoiding sudden brake failures that would compromise vehicle safety.
Solution Approach 2:
The system implements feedback by continuously monitoring motor current signals and comparing them against predicted failure patterns. The controller receives real-time current signal feedback from sensors and adjusts its assessment of motor health based on this ongoing information, allowing it to detect degradation trends before complete failure occurs.
2Reliability
If the system predicts motor failure in advance, then the reliability of the brake system is improved, but the device complexity increases due to additional sensors and control algorithms
Solution Approach 1:
The system uses the motor's own current signals for self-diagnosis and failure prediction. Instead of requiring separate diagnostic sensors, the existing motor current sensors serve dual purposes: normal operation monitoring and failure prediction, eliminating the need for additional hardware while improving reliability.
Solution Approach 2:
The motor current sensors perform multiple functions: they monitor motor operation during normal braking, provide data for machine learning-based failure prediction, and enable real-time assessment of motor health. This multi-functionality reduces the need for dedicated diagnostic equipment while enhancing system reliability.
3Measurement precision
If the system uses machine learning models for failure prediction, then the measurement precision of motor state detection is improved, but the loss of time for data processing increases
Solution Approach 1:
The machine learning models are trained in advance using historical motor data and failure patterns. This preliminary training phase allows the system to develop ready-to-use prediction algorithms that can quickly assess motor health in real-time without requiring complex calculations during actual braking operations, thus maintaining both accuracy and speed.
Data Source
AI summary
A brake system includes a sensor module including a motor current sensor and a force sensor, electric mechanical brake units mounted to wheels of a vehicle and including motors, respectively, and a controller configured to control one or more of the electric mechanical brake units, and the controller predicts states one of the motors of the electric mechanical brake units based on current signals of the motors detected by the motor current sensor, when at least one of the predicted states of the motors indicates that at least one of the motors fails, determines a failure level of the failed at least one of the motors based on sensor data obtained from the sensor module, calculates a requested torque of each of the wheels based on the determined failure level of the failed at least one of the motors, and controls a torque of each of the wheels based on the calculated requested torque of each of the wheels.