Column EPS Motor Abnormality Detection Using Neural Output Estimation
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Solution Overview
Problem
Conventional column electric power steering (EPS) systems lack effective methods for predicting motor degradation before failure, relying on post-failure diagnosis and insufficiently addressing decreases in motor output.
Innovation Solution
A device and method using an artificial neural network model, specifically a generative adversarial network (GAN), to detect motor abnormalities in column EPS systems by comparing estimated motor output with actual measurements, utilizing operation and state data from a vehicle's CAN network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional current-based fail-safety methods are used in column EPS systems, then the system can detect motor failures after they occur, but the system cannot predict motor degradation before failure and has insufficient direct response to output decrease
Solution Approach 1:
The patent applies preliminary action by using an artificial neural network model to predict motor output values before actual failure occurs. The system continuously monitors motor operation data and compares predicted values with actual measurements, enabling early detection of degradation trends. This allows maintenance to be scheduled proactively rather than reacting to failures after they happen, thus resolving the contradiction between reliability and time loss.
2Ease of repair
If post-failure diagnosis methods are used, then the system can identify motor failures after they occur, but the system cannot prevent accidents caused by steering failure
Solution Approach 1:
The patent implements preliminary anti-action by detecting motor degradation trends before complete failure occurs. The artificial neural network model predicts motor output and compares it with actual measurements, identifying deviations that indicate degradation. This early warning system allows for preventive maintenance or replacement, directly counteracting the harmful effect of potential steering failure and reducing accident risk before they can occur.
3Productivity
If conventional monitoring methods are used, then the system can operate the motor, but the system lacks effective methods for predicting motor degradation
Solution Approach 1:
The patent replaces conventional current-based monitoring with an artificial neural network-based predictive system. Instead of relying on simple current thresholds, the system uses machine learning models that analyze multiple operation data parameters to predict motor output and detect degradation trends. This substitution significantly improves measurement precision for degradation detection while maintaining continuous motor operation, resolving the contradiction between productivity and measurement precision.
Data Source
AI summary
Disclosed are a device and method for detecting an abnormality of a motor of a column electric power steering (EPS), and a non-transitory computer-readable storage medium storing a program for performing the method. The device for detecting the abnormality of the motor of the column EPS is a device for detecting an abnormality of a motor of a column EPS, which detects an abnormality of a motor of a column EPS that provides an auxiliary steering force to a steering column of an EPS system of a vehicle, and includes a memory configured to store one or more instructions, and a processor configured to execute the one or more instructions, wherein the processor executes the one or more instructions to input operation data related to steering wheel operation by a driver of the vehicle and state data indicating a state of the vehicle into an artificial neural network model, obtain an output estimated value of the motor output from the artificial neural network model, compare the estimated value with an output measurement value of the motor, and detect an abnormality of the motor.