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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, the 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 (CWRU), and we establish a ball bearing status recognition model according to different signal-captured positions. First, the infinite impulse response (IIR) filter and approximate entropy (ApEn) are used to extract the features of the signals. Afterwards, the data extracted from the features are used for model establishment and training through a back propagation neural network (BPNN) and a support vector machine (SVM). In general, the SVM classification is better than the BPNN, but through a series of experimental methods, we confirmed that the optimal BPNN parameters of this sample, including training function, data training ratio, and the number of neurons, make the recognition rate of the BPNN higher than that of the general SVM, and the accuracy rate reaches 95%.

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