Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

• Main Authors: Liang Guo, Hongli Gao, Haifeng Huang, Xiang He, ShiChao Li
• Format: Article
• Language: English
• Published: Hindawi Limited 2016-01-01
• Series: Shock and Vibration
• Online Access: http://dx.doi.org/10.1155/2016/4632562

Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.