Machine fault diagnosis based on Gaussian mixture model and its application

This paper presents a simple and efficient machine fault diagnosis approach based on Gaussian mixture model (GMM). After feature vectors that represent different machine conditions are extracted, a GMM for each of the machine conditions is built based on the corresponding extracted feature vectors, machine fault diagnosis can be accomplished through finding out the GMM whose posteriori probability for a given testing feature vector is the maximum of all. Experimental results based on the application on bearing fault diagnosis have shown that GMM can reliably diagnose not only the type of bearing faults, but also the degree of fault severity that are associated with incipient faults, moderate faults, and severe faults. Meanwhile, GMM has better diagnostic performance as compared to the multilayer perceptron neural networks.