Brake Fault Diagnosis Using Spectrogram-Based Deep Learning on NVH Signals
Brake fault diagnosis is critical for enhancing the safety and reliability of vehicles. This paper presents an approach of utilizing spectrogram deep learning, where the spectrogram is defined as the representation of the signal in terms of timefrequency plane. To diagnose faults in NVH signals, a novel framework combining deep learning models and unsupervised clustering methods is proposed, especially focusing on spectrograms. Raw multi-channel NVH data are divided into overlapping temporal segments and converted into timefrequency spectrogram images via Short-Time Fourier Transform (STFT). Afterward, intrinsic patterns of vibrations are extracted by K-means clustering, and a two-dimensional convolutional neural network (2D CNN) is trained based on the extracted clusters. Experimental datasets are formed by converting NVH signals into 37,962 spectrogram images through 38 sensor channels. Classification accuracy, precision, recall, and F1-score values reached up to 98.41% and more than 0.98 for all clusters, and the areas under ROC curves are close to 1.0 for all clusters.