Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification |
| |
Affiliation: | 1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China;2. Chongqing Engineering Laboratory for Detection Control and Integrated System, Chongqing Technology and Business University, Chongqing 400067, China;3. Chongqing Key Laboratory of Electronic Commerce and Supply Chain, Chongqing Technology and Business University, Chongqing 400067, China;4. Department of Mechanical Engineering, Universidad Politécnica Salesiana, Cuenca, Ecuador;1. State Key Laboratory of Robotics and System, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, Heilongjiang Province, China;2. School of Computer Science and Technology, Harbin Institute of Technology, No. 92 Xidazhi Street, Harbin 150001, Heilongjiang Province, China;3. Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore |
| |
Abstract: | Rolling bearing tips are often the most susceptible to electro-mechanical system failure due to high-speed and complex working conditions, and recent studies on diagnosing bearing health using vibration data have developed an assortment of feature extraction and fault classification methods. Due to the strong non-linear and non-stationary characteristics, an effective and reliable deep learning method based on a convolutional neural network (CNN) is investigated in this paper making use of cognitive computing theory, which introduces the advantages of image recognition and visual perception to bearing fault diagnosis by simulating the cognition process of the cerebral cortex. The novel feature representation method for bearing data is first discussed using supervised deep learning with the goal of identifying more robust and salient feature representations to reduce information loss. Next, the deep hierarchical structure is trained in a robust manner that is established using a transmitting rule of greedy training layer by layer. Convolution computation, rectified linear units, and sub-sampling are applied for weight replication and reducing the number of parameters that need to be learned to improve the general feed-forward back propagation training. The CNN model could thus reduce learning computation requirements in the temporal dimension, and an invariance level of working condition fluctuation and ambient noise is provided by identifying the elementary features of bearings. A top classifier followed by a back propagation process is used for fault classification. Contrast experiments and analyses have been undertaken to delineate the effectiveness of the CNN model for fault classification of rolling bearings. |
| |
Keywords: | Fault diagnosis Convolutional neural network Rolling bearing |
本文献已被 ScienceDirect 等数据库收录! |
|