Fault Diagnosis of Rolling Bearing Based on WP Reconstructed Energy Entropy and PSO-LSSVM

2019 ◽  
Author(s):  
Hongmei Yan ◽  
Huina Mu ◽  
Xiaojian Yi ◽  
Yuanyuan Yang ◽  
Guangliang Chen
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Energy Entropy

Fault Diagnosis to a Rolling Bearing in a Blowing Machine Based on the Theories of EMD and Intrinsic Modal Energy Entropy

2011 ◽  
Vol 284-286 ◽  
pp. 2461-2464
Author(s):  
Hai Lan Liu ◽  
Xiao Ping Li ◽  
Yan Nian Rui
Keyword(s):  
Fault Diagnosis ◽  
Classification System ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
New Method ◽  
Support Vector ◽  
Rolling Bearings ◽  
System A ◽  
Energy Entropy

Based on the research of the theory and the experiment of EMD and Intrinsic Modal Energy Entropy，the vibration signal of a rolling bearing in a Blowing Machine of a certain factory was measured when working. Then the signal was decomposed by EMD, its Intrinsic Modal Energy Entropy was calculated and used as fault feature. Finally, using a Support Vector Classification System, a satisfied effect of fault diagnosis of a rolling bearing in a Blowing Machine was got. The experiment had confirmed that the method was advanced, reliable and practical. A new method was provided for fault diagnosis of rolling bearings in some Blowing Machines.

scholarly journals A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method

2019 ◽  
Vol 9 (11) ◽  
pp. 2356 ◽  
Author(s):  
Yinsheng Chen ◽  
Tinghao Zhang ◽  
Zhongming Luo ◽  
Kun Sun
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Sample Entropy ◽  
Analysis Method ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy ◽  
Fault Severity ◽  
Fault Features

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%.

scholarly journals Composite Fault Diagnosis of Rolling Bearing Based on Optimized Wavelet Packet AR Spectrum Energy Entropy Combined with Adaptive No Velocity Term PSO-SOM-BPNN

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hongwei Fan ◽  
Yang Yan ◽  
Xuhui Zhang ◽  
Xiangang Cao ◽  
Jiateng Ma
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Back Propagation Neural Network ◽  
Spectral Energy ◽  
Self Organizing Map ◽  
Learning Factor ◽  
Energy Entropy

Aiming at the problem of low diagnosis efficiency and accuracy, due to noise and cross aliasing among various faults when diagnosing composite faults of rolling bearing under actual working conditions, a composite fault diagnosis method of rolling bearing based on optimized wavelet packet autoregressive (AR) spectral energy entropy and adaptive no velocity term particle swarm optimization-self organizing map-back propagation neural network (ANVTPSO-SOM-BPNN) is proposed. The energy entropy feature is extracted from the bearing vibration signal through wavelet packet AR spectrum, and SOM and BPNN are combined to form a series network. For PSO, the velocity term is discarded and the inertia weight and learning factor are adaptively adjusted. Finally, the Dempster-Shafer (D-S) evidence fusion diagnosis is carried out. To get closer to the application condition, the data are collected near and far away from the fault point for the composite fault diagnosis, which verifies the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document