Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

• Published in: Shock and vibration Vol. 2016; no. 2016; pp. 1 - 10
• Main Authors: He, Xiang, Huang, Haifeng, Gao, Hongli, Guo, Liang, Li, ShiChao
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2016; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Analysis; Bearing; Computational linguistics; Language processing; Maintenance; Natural language interfaces; Neural networks; Nonlinearity; Recognition; Reduction; Vibration
• ISSN: 1070-9622; 1875-9203
• DOI: 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.