Fault Diagnosis Method Based On Joint Distributional Adaptation For Intelligent Fault Diagnosis Under Different Working Conditions With Imbalanced Data

Rotating parts are the heart of the machinery industry, so various industries put forward higher requirements for the stability and reliability of mechanical work. However, relying on manual fault diagnosis is not only time-consuming and laborious, but also the accuracy rate is not ideal, so machine learning was introduced to the machinery fault diagnosis industry, but the training of fault diagnosis models depends on a large amount of data, and the working conditions of mechanical faults are variable, and it is difficult to apply the model trained under one working condition to another working condition. Therefore, we introduce the domain adaptive method of migration learning in fault diagnosis. However, most of the samples in real production are normal samples, and fault samples only account for a few. The sample imbalance problem needs to be solved urgently, so we adopt the method based on joint distribution adaptation, which firstly uses the model itself to make a priori judgment, and learns on the basis of the judgment; moreover, in order to solve the problem of ignoring the small samples and weight bias when the model samples are imbalanced, we propose the inverse sparsity loss function and entropy increasing loss function. Under the operating conditions of the bearing sample imbalance variable, fault tests are designed to verify the accuracy and effectiveness of the proposed method to solve the sample imbalance problem.