摘要：How to improve the accuracy and algorithm efficiency of bearing fault diagnosis has been the focus and hot topic in fault diagnosis field. Deep belief network is a typical deep learning method, which can be used to form a much higher-level abstract representation and find the distributed characteristics of data. In this article, a new method of bearing fault diag?nosis is proposed based on Teager–Kaiser energy operator and the particle swarm optimization-support vector machine with deep belief network. In this method, the demodulation signal is obtained using Teager–Kaiser energy operator first. And then the time and frequency statistic characteristic of the demodulation signal is analyzed. Furthermore, the deep belief network is used to extract time and frequency feature extraction. Finally, the extracted parameters are classified by particle swarm optimization-support vector machine. The experimental results show that it not only has higher accu?racy but also shortens the training time greatly, and it improves the accuracy and efficiency of fault diagnosis obviously.
关键词：Fault diagnosis; deep belief networks; Kaiser energy operator; support vector machine; bearing
