A new wind turbine fault diagnosis method based on the local mean decomposition

Liu, W.Y.; Zhang, W.H.; Han, Junyu; Wang, G.F.

doi:10.1016/j.renene.2012.05.018

Cited by 131 publications

(71 citation statements)

References 20 publications

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“…1b. The fault diagnoses sensors used and techniques employed for CMS on various parts of the WT are summarized in Table 1 [8][9][10][11][12]. Digital filtering, modeling, signal and spectrum analysis are the main parts of the data processing in CMS.…”

Section: Fault Diagnosis and Prognosis Systems On Wtmentioning

confidence: 99%

SCADA data based condition monitoring of wind turbines

2014

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Wind turbines (WTs) are quite expensive pieces of equipment in power industry. Maintenance and repair is a critical activity which also consumes lots of time and effort, hence making it a costly affair. Carefully planning the maintenance based upon condition of the equipment would make the process reasonable. Mostly the WTs are equipped with some kind of condition monitoring device/system, which provides the information about the device to the central data base i.e., supervisory control and data acquisition (SCADA) data base. These devices/systems make use of data processing techniques/methods in order to detect and predict faults. The information provided by condition monitoring equipments keeps on recoding in the SCADA data base. This paper dwells upon the techniques/methods/algorithms developed, to carry out diagnosis and prognosis of the faults, based upon SCADA data. Subsequently data driven approaching for SCADA data interpretation has been reviewed and an artificial intelligence (AI) based framework for fault diagnosis and prognosis of WTs using SCADA data is proposed.

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Section: Fault Diagnosis and Prognosis Systems On Wtmentioning

confidence: 99%

SCADA data based condition monitoring of wind turbines

2014

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“…And then the operation states of bearing have been identified by computing the instantaneous frequency of PFs and compared the identification performance with the EMD method. 27,28 Liu et al 29 have used LMD method to decompose the vibration signal of generator fault into several PFs and realized the early period fault diagnosis of generator in low-speed vibration of motor. In order to select useful PFs to describe the operation state of machine, a new fault diagnosis method combining LMD with FFT is proposed.…”

Section: Introductionmentioning

confidence: 99%

A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

Wang

2018

Journal of Low Frequency Noise, Vibration and Active Control

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Rolling bearing is one of the most crucial components in rotating machinery and due to their critical role, it is of great importance to monitor their operation conditions. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. Therefore, signal denoising preprocessing has become an essential part of condition monitoring and fault diagnosis. In the present study, a hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing is proposed. First, as a denoising preprocessing method, singular value difference spectrum denoising is applied to reduce the noise of the bearing vibration signal and improve the signal-to-noise ratio. Then, local mean decomposition method is used to decompose the denoised signals into several product functions. And product functions corresponding to the fault feature are selected according to the correlation coefficient criterion. Finally, Teager energy spectrum is analyzed by applying the Teager energy operator to the constructed amplitude modulation component. The proposed method is successfully applied to analyze the vibration signals collected from an experimental motive rolling bearing and rolling bearing of the self-made rotor experimental platform. The experimental results demonstrate that the proposed singular value difference spectrum denoising and local mean decomposition method can achieve fairly or slightly better performance than the normal local mean decomposition-Teager energy operator method, fast kurtogram, and the wavelet denoising and local mean decomposition method.

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“…Chen et al [8] employed LMD for rolling bearing and gear fault diagnosis. Liu et al [9] obtained a wind power generator's vibration signal instantaneous frequency via LMD to monitor the wind power generator's state. EMD and LMD have been widely deployed to extract fault features.…”

Section: Introductionmentioning

confidence: 99%

A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine

et al. 2017

Applied Sciences

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Abstract:Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine (KELM) is proposed in this paper. A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated. An eigenvector is created from the approximate entropy of each component. A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters. Finally, the effectiveness of the diagnosis method proposed in this paper is verified via a fan bearing fault diagnosis test. Under identical conditions, the result is compared with the results obtained using a back propagation (BP) neural network, a conventional extreme learning machine (ELM), and a support vector machine (SVM). The test result shows that the method proposed in this paper is superior to the other three methods in terms of diagnostic accuracy.

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A new wind turbine fault diagnosis method based on the local mean decomposition

Cited by 131 publications

References 20 publications

SCADA data based condition monitoring of wind turbines

SCADA data based condition monitoring of wind turbines

A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine

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