ISSN (print) 0914-4935
ISSN (online) 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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S&M2111 Research Paper of Special Issue

Optimizing the Back Propagation Neural Network Parameters to Judge the Fault Types of Ball Bearings

Bo-Lin Jian, Cai-Wan Chang-Jian, Yu-Syong Guo, Kuan-Ting Yu, and Her-Terng Yau

(Received Februrary 1, 2019; Accepted December 13, 2019)

Keywords: ball bearing, back propagation neural network, support vector machine, fault detection, approximate entropy

When current technology keeps advancing, global machine tool manufacturers are gradually moving toward smart production lines. The ball bearing is an important fixed part of a rotating shaft; its key function is to bear the load acting on the shaft and maintain the center position of the shaft. If the bearing is damaged, there will be abnormal vibration, runout, and abnormal noise. Hence, fault detection and recognition of the ball bearing are particularly important. The fault signal data of the ball bearing used in this study are obtained from the Case Western Reserve University and establish a ball bearing status recognition model according to different signal-captured in positions. First of all, the infinite impulse response filter and approximate entropy are used to extract the features of the signals. Afterwards, the data extracted from the features are used for model establishment and training through back propagation neural network and support vector machine. In general, the support vector machine classification is better than the back propagation neural network, but through a series of experimental methods, we confirmed that the optimal back propagation neural network parameters of this sample, including training function, data training ratio and parameters of neuron number, enable the recognition rate of the back propagation neural network better than that of the general support vector machine, and the accuracy rate becomes 95%.

Corresponding author: Her-Terng Yau




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