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A Deep Learning-Based Log-Mel Spectrogram-Utilized Fault Diagnosis Algorithm for In-Wheel Motor-Driven Vehicles
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DOI：10.3795/KSME-A.2026.50.4.267.png)
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Abstract
En 中文
This study presents a fault diagnosis method utilizing deep learning-based vibration signal analysis to ensure the safety of electric vehicles equipped with in-wheel motors (IWMs). IWMs are susceptible to road-induced impacts, which can degrade power performance and compromise vehicle safety. Consequently, three-axis accelerometers were installed on the vehicle's upper arm, brake caliper, and mounting bolts to collect vibration data under normal-road and speed-bump driving conditions. The normal condition was defined as stable operation without abnormalities, while the fault condition included electrical and mechanical faults such as inverter damage and W-phase short circuits. The acquired vibration signals were processed using a bandpass filter and converted into log-Mel spectrograms, which were then classified using a Convolutional Neural Network (CNN) model with transfer learning. Experimental results from real vehicle tests demonstrated an average classification accuracy of 98.80% for four driving condition classes: normal-road, normal-bump, fault-road, and fault-bump. These results confirm the applicability of deep learning-based vibration analysis for IWM fault diagnosis.
Keywords:
In-Wheel Motor Electric Vehicle
Vibration-Based Fault Diagnosis
Log-Mel Spectrogram
Deep Learning
Transfer Learning
Convolutional Neural Network
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0.2
Papers:
87
Citations:
308
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