Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

Su, Wensheng; Wang, Fengtao; Zhu, Hong Tao; Zhang, Zhixin; Guo, Zhenggang

doi:10.1016/j.ymssp.2009.11.011

Cited by 189 publications

(107 citation statements)

References 28 publications

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“…Lu, et al [52], applied GA to search the optimal multi-wavelets from an adaptive multi-wavelet library. Combination of optimal Morlet wavelet and autocorrelation analysis was used to extract the early stage fault of rolling bearings, and GA was employed to optimize the filtering parameters of the Morlet wavelet [53]. Some related research was conducted in the author's laboratory.…”

Section: Fault Feature Extraction Using Easmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Section: Fault Feature Extraction Using Easmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…One of the most popular fault analysis methods today is based on spectrum analysis [19][20][21]. Under the prior knowledge of the components presented, by the amplitude spectrum or the envelope spectrum, if the fault character frequency is distinct, it can be assessed that the corresponding fault may be immersed.…”

Section: Experimental Equipmentmentioning

confidence: 99%

“…In addition, if the signal is contaminated by noise, the character frequency is not easy to be extracted, and some preconditioning technologies should be adopt to enhance the signal. In the paper [19], the optimal Morlet wavelet filter and autocorrelation enhancement technologies are used before the envelope spectrum is performed. In the recent paper [20], the cyclic wiener filter method is employed to improve the weak fault feature before envelope spectrum analysis.…”

Section: Experimental Equipmentmentioning

confidence: 99%

Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

et al. 2013

Shock and Vibration

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Abstract. Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM) with particle swarm optimization (PSO) algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN), ant colony optimization artificial neural network (ANT-ANN), RVM, and support vector machines with particle swarm optimization (PSO-SVM), respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vectors, and the former is about 1/12-1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.

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“…Experimental studies using rolling bearings that contain different types of structural defects have confirmed that the developed new technique enables high signal-to-noise ratio for effective machine defect identification. (Su et al, 2010) developed a new autocorrelation enhancement algorithm including two aspects of autocorrelation and extended Shannon function. This method does not need to select a threshold and can be implemented in an automatic way and is realized in various stages.…”

Section: Bearingsmentioning

confidence: 99%

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

Λούτας¹,

Kostopoulos²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

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Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement

Cited by 189 publications

References 28 publications

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

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