A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion

Qu, Jinxiu; Zhang, Zhousuo; Gong, Teng

doi:10.1016/j.neucom.2015.07.020

Cited by 120 publications

(58 citation statements)

References 39 publications

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“…In order to extract useful features for diagnosis, it is necessary to eliminate the noise in the original vibration signal. Dualtree complex wavelet packet transform (DTCWPT) is a multiscale method with such attractive properties as nearly shift-invariance and reduced aliasing, which has been widely used in signal processing [26]. In this study, DTCWPT is employed to denoise the original vibration signal combined with the threshold method, and Hilbert transform envelope algorithm is applied to extract the fault characteristic frequency.…”

Section: Condition Monitoring and Performance Degradationmentioning

confidence: 99%

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

She

Tian

Liang

et al. 2018

Shock and Vibration

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The dimension reduction methods have been proved powerful and practical to extract latent features in the signal for process monitoring. A linear dimension reduction method called nonlocal orthogonal preserving embedding (NLOPE) and its nonlinear form named nonlocal kernel orthogonal preserving embedding (NLKOPE) are proposed and applied for condition monitoring and fault detection. Different from kernel orthogonal neighborhood preserving embedding (KONPE) and kernel principal component analysis (KPCA), the NLOPE and NLKOPE models aim at preserving global and local data structures simultaneously by constructing a dual-objective optimization function. In order to adjust the trade-off between global and local data structures, a weighted parameter is introduced to balance the objective function. Compared with KONPE and KPCA, NLKOPE combines both the advantages of KONPE and KPCA, and NLKOPE is also more powerful in extracting potential useful features in nonlinear data set than NLOPE. For the purpose of condition monitoring and fault detection, monitoring statistics are constructed in feature space. Finally, three case studies on the gearbox and bearing test rig are carried out to demonstrate the effectiveness of the proposed nonlinear fault detection method.

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Section: Condition Monitoring and Performance Degradationmentioning

confidence: 99%

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

She

Tian

Liang

et al. 2018

Shock and Vibration

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“…S. Mallat proposed the concept of multi-resolution analysis when constructing orthogonal wavelet bases, and was first applied to wavelet analysis. Wavelet analysis is also mature in the field of fault diagnosis at the present stage [6][7][8]. The multiresolution analysis wavelet decomposition tree is shown in Fig.…”

Section: Wavelet Multiresolution Analysismentioning

confidence: 99%

Engine fault diagnosis based on multi-resolution analysis and dual microphones

Zeng¹,

Zeng²,

Mei³

et al. 2018

Vib. proced.

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In order to diagnose the abnormal sound fault of the diesel without disassemble, dual microphones are used to collect noise signal of the engine in this paper, and the sound pressure signal is decomposed with wavelet decomposition technique. By comparing different wavelet decomposition techniques, it determines the sound feature parameter for fault diagnosis, and establishes a BP neural network for pattern recognition. The test result shows that diagnosing engine fault with dual microphones is accuracy, and it can be applied to diagnose the abnormal sound fault of engine.

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“…The role of the decision fusion strategy is to combine the decisions of various base classifiers to enhance the final classification results and reduce decision conflicts. The effectiveness of the decision fusion theory had been verified in many studies [7][8][9]. Basic decision fusion strategies include the voting method, the weighted voting method, Bayesian theory, and the Dempster-Shafer evidence theory [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Fault Classification Decision Fusion System Based on Combination Weights and an Improved Voting Method

Zeng

Zhou

et al. 2019

Processes

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It is difficult to correctly classify all faults by using only one classifier, and the performance of most classifiers varies under different conditions. In view of this, a new decision fusion system is proposed to solve the problem of fault classification. The proposed decision fusion system is innovative in two aspects: the use of combined weights and a new improved voting method. The combined weights integrate the subjective and objective weights, where the analytic hierarchy process and entropy weight-technique for order performance by similarity to ideal solution are used to determine the subjective and objective weights of different base classifiers under multiple performance evaluation indicators. Moreover, a new improved voting method based on the concept of classifier validity is proposed to increase the accuracy of the decision system. Finally, the method is validated by the Tennessee Eastman benchmark process, and the classification accuracy of the new method is shown to be improved by more than 5.06% compared to the best base classifier.

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A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion

Cited by 120 publications

References 39 publications

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

Nonlinear Model for Condition Monitoring and Fault Detection Based on Nonlocal Kernel Orthogonal Preserving Embedding

Engine fault diagnosis based on multi-resolution analysis and dual microphones

Fault Classification Decision Fusion System Based on Combination Weights and an Improved Voting Method

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