Zero-shot learning compound fault diagnosis of bearings

Abstract

The compound fault signal of bearings is coupled and complex, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples. In the industrial scenarios, collecting and labeling sufficient compound fault samples are unpractical. Using the model trained on single fault sample to identify unknown compound fault is challenging and innovative. To address this problem, we propose a Zero-shot Learning Compound Fault Diagnosis Model of bearing (ZLCFDM). First, we design a semantic encoding method to express the semantic vectors of single fault and compound fault according to the fault characteristics. Second, a convolutional neural network is designed to extract the time-frequency visual features of compound fault signal. Then we embed the semantic vector of the fault into the visual space of the fault data. The cosine distance is merged into K-nearest neighbor (KNN) to measure the distance between the visual features and the semantic vectors of the compound faults, such that the model can identify the categories of unknown compound faults. To validate the proposed method, we conduct experiments on self-built testbed. The results demonstrate that the identification accuracy of compound fault can reach 77.73% when the model trained without any compound fault samples. This is the first time to propose the compound fault diagnosis of bearing base on zero-shot learning.