A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

Abstract

To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper. A fast sample entropy calculation method based on a kd tree is adopted to improve the real-time performance of fault detection in this paper. In view of the non-linearity and non-stationarity of the vibration signals, the vibration signal of the rolling bearing is decomposed into several sub-signals containing fault information by using a wavelet packet. Then, the energy entropy values of the sub-signals decomposed by the wavelet packet are calculated to generate the feature vectors for describing different fault types and severity levels of rolling bearings. The multiclass relevance vector machine modeled by the feature vectors of different fault types and severity levels is used to realize fault type identification and a fault severity analysis of the bearings. The proposed fault diagnosis and severity analysis method is fully evaluated by experiments. The experimental results demonstrate that the fault detection method based on the sample entropy can effectively detect rolling bearing failure. The fault feature extraction method based on the wavelet packet energy entropy can effectively extract the fault features of vibration signals and a multiclass relevance vector machine can identify the fault type and severity by means of the fault features contained in these signals. Compared with some existing bearing rolling fault diagnosis methods, the proposed method is excellent for fault diagnosis and severity analysis and improves the fault identification rate reaching as high as 99.47%.

Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA

“…At the same time, it can be found that, in the nodes [2,0], the energy difference is almost zero, which is the same as in the first layer of nodes [1,0]. Although the energy difference of nodes [2,3] fluctuates, it does not exceed the set threshold, so it is considered that the change is mainly caused by the noise contained in the data. In Figure 12, node [3,0] and node [1,0] have no fault data as in [2,0].…”

“…In Figure 11, the energy of wavelet packet of node [2,1] detects data faults at about 225-275 data points, and the range of data faults detected by node [2,2] is about 200-275, 300-350 data points. In the nodes [2,0] and [2,3], there is no fault information. At the same time, it can be found that, in the nodes [2,0], the energy difference is almost zero, which is the same as in the first layer of nodes [1,0].…”

“…At the same time, due to the inconsistency of the front-end sensors and the changeability of the natural environment, the traffic data obtained contains not only normal traffic information, but also abnormal data caused by electromagnetic interference, detector failure, transmission network failure and traffic accidents-thus affecting the reliability of the intelligent transportation system (ITS) [2]. Therefore, it is of great practical significance to carry out research on the fault data diagnosis technology of traffic detectors to effectively improve the reliability of ITS operations [3].…”

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

“…The failures of rolling bearings often develop from the normal stage to the incipient stage, then enter the stage of repeated failure, and finally reach the stage of complete breakdown. The serious faults do not occur in an instant, but has a process of gradual deterioration [5][6][7]. Therefore, it is of great significance to study the incipient fault detection of rolling bearings, find the incipient signal characteristics of faults, and eliminate the safety hazards in time when the fault has not developed toward a serious degree [8,9].…”

The Average Coding Length of Huffman Coding Based Signal Processing and Its Application in Fault Severity Recognition

“…Recently, the importance of bearing fault severity recognition has been paid more and more attention [2,3]. In general, the damage severity of bearings is usually measured from the aspect of signal complexity [4].…”