Anomaly detection in rolling bearings based on the Mel‐frequency cepstrum coefficient and masked autoencoder for distribution estimation

It is difficult to establish a classification and recognition model of machinery and equipment based on labeled samples in the actual industrial environment because of the imperfect fault modes and data missing. To solve this problem, a semisupervised anomaly detection method based on masked autoencoders of distribution estimation (MADE) is designed. First, the Mel‐frequency cepstrum coefficient (MFCC) is employed to extract fault features from vibration signals of rolling bearings. Then, a group of mask matrices are set on each hidden layer to overcome the perfect reconstruction problem of the autoencoders' input, and the full‐connection probability of reconstruction is used to replace the reconstruction error and adopted as the anomaly score. Finally, the diagnostic threshold is determined according to the Youden index. Experimental results show that the MADE method can extract fault‐sensitive features from a noisy industrial environment and introduce mask matrices renders to make the network autoregressive, thus solving the problem of perfect reconstruction of autoencoders. It is verified based on three rolling bearing datasets that the accuracy, precision, recall, and F1‐score of the proposed method are confirmed to be all 100%. Moreover, the accuracy of the proposed method is 17.19% higher than that of the memory‐inhibition method on the rolling bearing dataset provided by the Center for Intelligent Maintenance Systems (IMS) in University of Cincinnati (USA). The accuracy of the proposed method is also improved compared with other state‐of‐the‐art anomaly detection methods.