Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor

Chen, H.X.; Chua, Patrick S. K.; Lim, G.H.

doi:10.1016/j.ymssp.2005.04.007

Cited by 59 publications

(30 citation statements)

References 14 publications

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“…Some adaptive techniques using Morelet wavelets were successfully proposed for rotating machine component fault detection, such as [17] and [18]. In this paper; an adaptive EMD is proposed; however, two major topics are addressed: (i) improving of the original EMD algorithm in order to enhance the appearance of the nominal frequencies, and (ii) proposing a model for both simulation and experiment data for bearing fault detection.…”

Section: Adaptive Empirical Mode Decomposition For Bearing Fault Detementioning

confidence: 99%

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Tuan

Nguyen

2016

SV-JME

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Section: Adaptive Empirical Mode Decomposition For Bearing Fault Detementioning

confidence: 99%

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Tuan

Nguyen

2016

SV-JME

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“…In contrast, the tree with more nodes (Fig. 4a) corresponds to the situation where the noise is concentrated on a frequency band (3)(4)(5) in which the plant output has the largest power, as shown in Fig. 2.…”

Section: Case Studymentioning

confidence: 99%

“…A wavelet model particularly suitable for adaptive identification of linear periodic time-varying systems was presented in [6]. Several applications can also be found in the context of mechanical systems, such as modal parameter identification [7,11] and vibration signal modelling for fault diagnosis purposes [3].…”

Section: Introductionmentioning

confidence: 99%

Wavelet-Packet Identification of Dynamic Systems with Coloured Measurement Noise

Paiva

Galvão

2008

Lecture Notes in Computer Science

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Abstract. This paper analyses the effect of coloured noise on a recently proposed technique for linear system identification in frequency subbands using wavelet packets. For this purpose, a simulation study involving the longitudinal dynamics of a flexible aircraft model is presented. The results reveal that the wavelet-packet identification outcome is robust with respect to changes in the spectral noise features. In particular, the identified frequency response is effectively smoothed in regions with poor signal-to-noise ratio. Finally, the results are favourably compared, in terms of resonance peak identification, with those obtained by standard time-domain identification methods.

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“…To this end, a considerable number of researches have focused on this field. Chen et al [1] utilized the vibration signal's energy obtained by the continuous wavelet transform, and Wang et al [2] employed the Hilbert transform and the genetic algorithm to obtain the characteristic frequencies of the vibration signals, which are used to determine the size and the position of the crack in plunger of water hydraulic motor. Zhao et al [3] proposed a method based on intermittent chaos and sliding window symbol sequence statistics for loose shoes early fault diagnosis of hydraulic pump.…”

Section: Introductionmentioning

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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Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor

Cited by 59 publications

References 14 publications

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Adaptive Empirical Mode Decomposition for Bearing Fault Detection

Wavelet-Packet Identification of Dynamic Systems with Coloured Measurement Noise

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

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