Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform

Wang, Yanxue; He, Zhengjia; Ye, Zi

doi:10.1016/j.ymssp.2009.06.015

Cited by 212 publications

(108 citation statements)

References 39 publications

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“…Let ( ) and ( ) represent the used two real-valued wavelets of DTCWT, and ( ) and ( ) are the corresponding scaling functions. A complex-valued wavelet ( ) can be denoted as [26]:…”

Section: Fig 1 Dtcwt Decomposition and Reconstructionmentioning

confidence: 99%

Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM

Ma¹,

Zhang²,

Fan³

2018

J. vibroeng.

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Abstract. In order to enhance the capability of feature extraction and fault classification of bearings, this study proposes a feature extraction approach based on dual-tree complex wavelet transform (DTCWT) and permutation entropy (PE), using the fuzzy c means clustering (FCM) to identify fault types. The vibration signal of bearings can be decomposed into several wavelet components with DTCWT which can describe the local characteristics of vibration signals accurately. And the PE of each wavelet component, which can describe the complexity of a time series, is calculated to be regarded as the fault features. Then forming the standard clustering centers by the FCM, we defined a standard using the Hamming approach degree to evaluate the classification results in the FCM. In order to verify the effectiveness of the proposed approach, compared with two other typical signal analysis methods: ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD), through extracting fault features, it required to identify the fault types and severities under variable operating conditions. The experimental results demonstrate that the proposed approach has a better accuracy and performance to diagnose a bearing fault under different fault severities and variable operating conditions. The proposed approach is suitable for a fault diagnosis due to its good ability to the feature extraction and fault classification.

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“…Let ( ) and ( ) represent the used two real-valued wavelets of DTCWT, and ( ) and ( ) are the corresponding scaling functions. A complex-valued wavelet ( ) can be denoted as [26]:…”

Section: Fig 1 Dtcwt Decomposition and Reconstructionmentioning

confidence: 99%

Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM

Ma¹,

Zhang²,

Fan³

2018

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…There are several mathematical methods to deal with vibration signals. They have chronologically evolved from frequency-domain analysis regarding digital filters [21] to wavelet transform denoising [22]. The wavelet transform method, applied in this paper, has the mathematical mechanisms to properly denoise and process the primary data acquired.…”

Section: Introductionmentioning

confidence: 99%

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Pino

Ribas

Guimarães

2018

Shock and Vibration

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The contribution of a medium-sized hydro power plant to the power grid can be either at base load or at peak load. When the latter is the most common operation mode, it increases the start and stop frequency, intensifying the hydro turbine components' degradation, such as the guide bearings. This happens due to more frequent operation in transient states, which means being outside the service point of the machines' nominal condition, consisting of speed, flow, and gross head. Such transient state operation increases the runner bearings' mechanical vibration. The readings are acquired during the runner start-ups and filtered by a DC component mean value and a wavelet packet transform. The filtered series are used to estimate the relationship between the maximum orbit curve displacement and the accumulated operating hours. The estimated equation associated with the ISO 7919-5 vibration standards establishes the sojourn times of the degradation states, sufficient to obtain the transition probability distribution. Thereafter, a triangular probability function is used to determine the observation probability distribution in each state. Both matrices are inputs required by a hidden Markov model aiming to simulate the equipment deterioration process, given a sequence of maximum orbit curve displacements.

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“…EMD has been used to denoise in a variety of applications [8,9] and also to detect tooth faults in gearbox [10]. NC as a denoising method has been used in various applications such as image denoising, [11] fault detection in rotating machinery [12]. AT has been used to denoise gear vibration signals and fault diagnosis and has been used extensively in CM of gears [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of wavelet based denoising schemes for gear condition monitoring: An Artificial Neural Network based Approach

Ahmed

Pai²,

Sriram

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Vibration Analysis has been extensively used in recent past for gear fault diagnosis. The vibration signals extracted is usually contaminated with noise and may lead to wrong interpretation of results. The denoising of extracted vibration signals helps the fault diagnosis by giving meaningful results. Wavelet Transform (WT) increases signal to noise ratio (SNR), reduces root mean square error (RMSE) and is effective to denoise the gear vibration signals. The extracted signals have to be denoised by selecting a proper denoising scheme in order to prevent the loss of signal information along with noise. An approach has been made in this work to show the effectiveness of Principal Component Analysis (PCA) to denoise gear vibration signal. In this regard three selected wavelet based denoising schemes namely PCA, Empirical Mode Decomposition (EMD), Neighcoeff Coefficient (NC), has been compared with Adaptive Threshold (AT) an extensively used wavelet based denoising scheme for gear vibration signal. The vibration signals acquired from a customized gear test rig were denoised by above mentioned four denoising schemes. The fault identification capability as well as SNR, Kurtosis and RMSE for the four denoising schemes have been compared. Features extracted from the denoised signals have been used to train and test artificial neural network (ANN) models. The performances of the four denoising schemes have been evaluated based on the performance of the ANN models. The best denoising scheme has been identified, based on the classification accuracy results. PCA is effective in all the regards as a best denoising scheme.

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Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform

Cited by 212 publications

References 39 publications

Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM

Novel bearing fault diagnosis model integrated with dual-tree complex wavelet transform, permutation entropy and optimized FCM

Bearing Diagnostics of Hydro Power Plants Using Wavelet Packet Transform and a Hidden Markov Model with Orbit Curves

Comparison of wavelet based denoising schemes for gear condition monitoring: An Artificial Neural Network based Approach

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