Fault Diagnosis of Rolling Bearing Using Wireless Sensor Networks and Convolutional Neural Network.

Liqun Hou; Zijing Li; Huaisheng Qu

Rolling bearings are widely used in modern production equipment. Effective bearing fault diagnosis method will improve the reliability of the machinery and increase its operating efficiency. In this paper, a novel fault diagnosis method based on WSN and CNN has been proposed to fully utilize the strong fault classification capability of CNN and the inherent merits of WSNs, such as relatively low cost, convenience of installation, and ease of relocation. The feasibility and effectiveness of proposed system are evaluated using the vibration data sets of seven motor operating conditions released by the Case Western Reserve University Bearing Data Center. The experimental results show the fault diagnosis accuracy of the proposed approach can reach 97.6%.

CASE Western Reserve University; CONVOLUTIONAL neural networks; FAULT diagnosis; WIRELESS sensor networks; ROLLER bearings; SIGNAL convolution; SERVER farms (Computer network management)

International Journal of Online & Biomedical Engineering, 2020, Vol 16, Issue 11, p32

2626-8493

Academic Journal

10.3991/ijoe.v16i11.15959