Rotating machinery fault diagnosis using signal-adapted lifting scheme

Li, Zhen; He, Zhengjia; Ye, Zi; Jiang, Hongkai

doi:10.1016/j.ymssp.2007.09.008

Cited by 35 publications

(21 citation statements)

References 12 publications

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“…It was concluded that PSVM has an edge over SVM in the classification efficiency of various fault conditions. (Li et al, 2008) presented a new signal-adapted lifting scheme for rotating machinery fault diagnosis, which allows the construction of a wavelet directly from the statistics of a given signal. The prediction operator based on genetic algorithms was designed to maximize the kurtosis of detail signal produced by the lifting scheme, and the update operator was designed to minimize a reconstruction error.…”

Section: Gearboxesmentioning

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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Section: Gearboxesmentioning

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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“…Unlike the traditional WT, the second generation wavelet (SGW) [22] is a flexible wavelet construction method which is independent of the Fourier transform. Applying SGW to the denoising pretreatment will provide a faster and a more effective algorithm than the traditional WT [23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter

et al. 2015

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Defective rolling bearing response is often characterized by the presence of periodic impulses. However, the in-situ sampled vibration signal is ordinarily mixed with ambient noises and easy to be interfered even submerged. The hybrid approach combining the second generation wavelet denoising with morphological filter is presented. The raw signal is purified using the second generation wavelet. The difference between the closing and opening operator is employed as the morphology filter to extract the periodicity impulsive features from the purified signal and the defect information is easily to be extracted from the corresponding frequency spectrum. The proposed approach is evaluated by simulations and vibration signals from defective bearings with inner race fault, outer race fault, rolling element fault and compound faults, respectively. Results show that the ambient noises can be fully restrained and the defect information of the above defective bearings is well extracted, which demonstrates that the approach is feasible and effective for the fault detection of rolling bearing.

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“…The lifting wavelet transform has been widely used in mechanical signal processing. For example, Li et al proposed a new signal-adapted lifting scheme for rotating machinery fault diagnosis, which was applied to analyze bearing and gearbox vibration signals [24]. Huang et al proposed an enhanced feature extraction model for machinery performance assessment, which is based on the lifting-based wavelet packet transform and sampling-importance-resampling methods [25].…”

Section: Introductionmentioning

confidence: 99%

Operational Reliability Assessment of Compressor Gearboxes with Normalized Lifting Wavelet Entropy from Condition Monitoring Information

Xiao-li

Wang

Cao

et al. 2015

Entropy

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Classical reliability assessment methods have predominantly focused on probability and statistical theories, which are insufficient in assessing the operational reliability of individual mechanical equipment with time-varying characteristics. A new approach to assess machinery operational reliability with normalized lifting wavelet entropy from condition monitoring information is proposed, which is different from classical reliability assessment methods depending on probability and statistics analysis. The machinery vibration signals with time-varying operational characteristics are firstly decomposed and reconstructed by means of a lifting wavelet package transform. The relative energy of every reconstructed signal is computed as an energy percentage of the reconstructed signal in the whole signal energy. Moreover, a normalized lifting wavelet entropy is defined by the relative energy to reveal the machinery operational uncertainty. Finally, operational reliability degree is defined by the quantitative value obtained by the normalized lifting wavelet entropy belonging to the range of [0,1]. The proposed method is applied in the operational reliability assessment of the gearbox in an oxy-generator compressor to validate the effectiveness.

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Rotating machinery fault diagnosis using signal-adapted lifting scheme

Cited by 35 publications

References 12 publications

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

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

Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter

Operational Reliability Assessment of Compressor Gearboxes with Normalized Lifting Wavelet Entropy from Condition Monitoring Information

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