scholarly journals Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zhang ◽  
Long Zhang ◽  
Junfeng Hu ◽  
Guoliang Xiong
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Sample Entropy ◽  
Classifier Ensemble ◽  
Detection Accuracy ◽  
Diagnosis System ◽  
Bearing Fault ◽  
Fault Recognition ◽  
Fault Severity ◽  
Lifting Wavelet

In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.

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 Roller Bearing Fault Diagnosis Based on Nonlinear Redundant Lifting Wavelet Packet Analysis

Sensors ◽  
2010 ◽  
Vol 11 (1) ◽  
pp. 260-277 ◽  
Author(s):  
Lixin Gao ◽  
Zijing Yang ◽  
Ligang Cai ◽  
Huaqing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Wavelet Packet Analysis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Lifting Wavelet

scholarly journals Research on Voltage Waveform Fault Detection of Miniature Vibration Motor Based on Improved WP-LSTM

Micromachines ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 753
Author(s):  
Ruirui Wang ◽  
Zhan Feng ◽  
Sisi Huang ◽  
Xia Fang ◽  
Jie Wang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Short Term Memory ◽  
Wavelet Packet ◽  
Engineering Practice ◽  
Detection Accuracy ◽  
Reconstructed Signal ◽  
Voltage Signal ◽  
Diagnosis Method ◽  
Lstm Network

To solve the problem of vibration motor fault detection accuracy and inefficiency in smartphone components, this paper proposes a fault diagnosis method based on the wavelet packet and improves long and short-term memory network. First, the voltage signal of the vibration motor is decomposed by a wavelet packet to reconstruct the signal. Secondly, the reconstructed signal is input into the improved three-layer LSTM network as a feature vector. The memory characteristics of the LSTM network are used to fully learn the time-series fault feature information in the unsteady state signal, and then, the model is used to diagnose the motor fault. Finally, the feasibility of the proposed method is verified through experiments and can be applied to engineering practice. Compared with the existing motor fault diagnosis method, the improved WP-LSTM diagnosis method has a better diagnosis effect and improves fault diagnosis.

scholarly journals Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Haodong Yuan ◽  
Jin Chen ◽  
Guangming Dong
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Fixed Number ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Learned Dictionary ◽  
Adaptive Pso ◽  
Diagnosis Method ◽  
Linear Svm

A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC) and adaptive PSO-optimized support vector machine (SVM) is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO) is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.

Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis

2003 ◽  
Vol 125 (1) ◽  
pp. 80-95 ◽  
Author(s):  
Kyusung Kim ◽  
Alexander G. Parlos
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Wavelet Packet ◽  
New Technology ◽  
False Alarms ◽  
Motor Speed ◽  
Diagnosis System ◽  
Model Based ◽  
Fault Diagnosis System ◽  
The Impact

Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance, and improved operational efficiency of induction motors. At the same time, reducing the probability of false alarms increases the confidence of equipment owners in this new technology. In this paper, a model-based fault diagnosis system recently proposed by the authors for induction motors is experimentally compared for fault detection and false alarm performance with a more traditional signal-based motor fault estimator. In addition to the nameplate information required for the initial set-up, the proposed model-based fault diagnosis system uses measured motor terminal currents and voltages, and motor speed. The motor model embedded in the diagnosis system is empirically obtained using dynamic recurrent neural networks, and the resulting residuals are processed using wavelet packet decomposition. The effectiveness of the model-based diagnosis system in detecting the most widely encountered motor electrical and mechanical faults, while minimizing the impact of false alarms resulting from power supply and load variations, is demonstrated through extensive testing with staged motor faults. The model-based fault diagnosis system is scalable to motors of different power ratings and it has been successfully tested with fault data from 2.2kW,373kW, and 597kW induction motors.

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