A NEW NOISE REDUCTION METHOD FOR FAULT DIAGNOSIS OF MOTORIZED SPINDLE ROLLING BEARING.

The bearing is the core part of the electric spindle, and the bearing failure directly affects the normal operation of the electric spindle. When the electric spindle breaks down, the mechanical system can not operate normally, which leads to great losses. In order to detect bearing faults, many traditional intelligent fault detection methods are proposed, and the fault category can not be accurately determined due to the existence of noise components in the signal. In this paper, a new noise reduction algorithm- Sparse Denoising Auto-Encode (SDAE) is proposed. The Denoising Auto-Encoder can learn the noise factors and extract the succinct expressions from raw data automatically, and sparsity is integrated on the basis of Denoising Auto-Encoder to improve the generalization of feature expression. More effective feature expressions are extracted to train Convolutional Neural Network (CNN), and the Adam optimization algorithm is used to fine-tune CNN to improve the accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]