Rolling-Element Bearing Fault Data Automatic Clustering Based on Wavelet and Deep Neural Network - Yanli Yang; Peiying Fu (Shock and Vibration)
A method based on wavelet and deep neural network for rolling-element bearing fault data automatic clustering is proposed. The method can achieve intelligent signal classification without human knowledge. The time-domain vibration signals are decomposed by wavelet packet transform (WPT) to obtain eigenvectors that characterize fault types. By using the eigenvectors, a dataset in which samples are labeled randomly is configured. The dataset is roughly classified by the distance-based clustering method. A fine classification process based on deep neural network is followed to achieve accurate classification. The entire process is automatically completed, which can effectively overcome the shortcomings such as low work efficiency, high implementation cost, and large classification error caused by individual participation. The proposed method is tested with the bearing data provided by the Case Western Reserve University (CWRU) Bearing Data Center. The testing results show that the proposed method has good performance in automatic clustering of rolling-element bearings fault data.