Convolutional Neural Network-Based Bayesian Gaussian Mixture for Intelligent Fault Diagnosis of Rotating Machinery

Abstract

Fault diagnosis is very important to ensure the efficiency and reliability of rotating machinery. Traditional fault diagnosis methods often require manual feature design and extraction, which is an exhausting work in industrial applications. Convolutional neural network (CNN)-based methods have presented a great potential to automatically extract and select representative features for fault diagnosis. In this article, a novel three-step intelligent fault diagnosis method is proposed based on CNN and Bayesian Gaussian mixture (BGM) for rotating machinery. In the fault dataset construction step, multiple binary training datasets are constructed to amplify the difference between one fault mode and the others using a defined fault labeling rule. In the fault feature extraction step, multiple binary classification tasks are, respectively, implemented to learn the representative features for each fault mode through a proposed group of CNN models based on the above-obtained training datasets. In the fault diagnosis step, multiple Gaussian mixture models (GMMs) are adopted to fit the data distributions of the learned fault features, and an BGM model based on GMM parameters and Bayesian network is designed to establish a cause-and-effect relationship between corresponding parameters and fault modes. Two case studies are used to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method has a superior performance over the existing methods.