Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

Isham, M. Firdaus; Leong, M. Salman; Lim, Meng Hee; Ahmad, Zair Asrar

doi:10.1002/we.2323

Cited by 33 publications

(29 citation statements)

References 51 publications

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“…In equation (6), x j i means the state vector of the visual layer; y j i means the input vector of original data; and m and n, respectively, represent the number of nodes and samples in the visible layer.…”

Section: Parameter Determining Criterion: Minimum Root Meanmentioning

confidence: 99%

“…Such method can effectively extract fault feature frequency, making accurate diagnosis for crack faults in gears under strong background noises and subtle fault signals. Isham et al [6] decomposed the vibration signal of the gearbox by VMD, then extracted the time-domain, frequency-domain, and time-frequency-domain features of each IMF component to construct the eigen matrix of signal, and finally trained ELM to establish a fault diagnosis model to complete the intelligent diagnosis of the gearbox in the wind turbine. Zhang et al [7] took advantage of GWO algorithm to search for the optimized parameters in TVF-EMD matching with the input signal, eliminating the influence of parameter selection on the decomposition results.…”

Section: Introductionmentioning

confidence: 99%

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A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox

Gai

Shen

Wang

et al. 2020

Shock and Vibration

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Aiming at the problems of poor self-adaptive ability in traditional feature extraction methods and weak generalization ability in single classifier under big data, an internal parameter-optimized Deep Belief Network (DBN) method based on grasshopper optimization algorithm (GOA) is proposed. First, the minimum Root Mean Square Error (RMSE) in the network training is taken as the fitness function, in which GOA is used to search for the optimal parameter combination of DBN. After that the learning rate and the number of batch learning in DBN which have great influence on the training error would be properly selected. At the same time, the optimal structure distribution of DBN is given through comparison. Then, FFT and linear normalization are introduced to process the original vibration signal of the gearbox, preprocess the data from multiple sensors and construct the input samples for DBN. Finally, combining with deep learning featured by powerful self-adaptive feature extraction and nonlinear mapping capabilities, the obtained samples are input into DBN for training, and the fault diagnosis model for gearbox based on DBN would be established. After several tests with the remaining samples, the diagnosis rate of the model could reach over 99.5%, which is far better than the traditional fault diagnosis method based on feature extraction and pattern recognition. The experimental results show that this method could effectively improve the self-adaptive feature extraction ability of the model as well as its accuracy of fault diagnosis, which has better generalization performance.

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Section: Parameter Determining Criterion: Minimum Root Meanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox

Gai

Shen

Wang

et al. 2020

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…Thus, on this basis, Shan et al [38] applied the minimization criterion of the root mean square error (RMSE) to adaptively search for two VMD parameters. Isham et al [39] adopted a differential evolution algorithm to select the optimized VMD parameter, where the statistical parameter ratio is utilized to construct the objective function. Zhu et al [40] used an artificial fish swarm algorithm to choose adaptively the combination parameters of VMD, where the kurtosis is used as the optimization index.…”

Section: Introductionmentioning

confidence: 99%

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

et al. 2020

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Variational mode decomposition (VMD) with a non-recursive and narrow-band filtering nature is a promising time-frequency analysis tool, which can deal effectively with a non-stationary and complicated compound signal. Nevertheless, the factitious parameter setting in VMD is closely related to its decomposability. Moreover, VMD has a certain endpoint effect phenomenon. Hence, to overcome these drawbacks, this paper presents a novel time-frequency analysis algorithm termed as improved adaptive variational mode decomposition (IAVMD) for rotor fault diagnosis. First, a waveform matching extension is employed to preprocess the left and right boundaries of the raw compound signal instead of mirroring the extreme extension. Then, a grey wolf optimization (GWO) algorithm is employed to determine the inside parameters ( α ^ , K) of VMD, where the minimization of the mean of weighted sparseness kurtosis (WSK) is regarded as the optimized target. Meanwhile, VMD with the optimized parameters is used to decompose the preprocessed signal into several mono-component signals. Finally, a Teager energy operator (TEO) with a favorable demodulation performance is conducted to efficiently estimate the instantaneous characteristics of each mono-component signal, which is aimed at obtaining the ultimate time-frequency representation (TFR). The efficacy of the presented approach is verified by applying the simulated data and experimental rotor vibration data. The results indicate that our approach can provide a precise diagnosis result, and it exhibits the patterns of time-varying frequency more explicitly than some existing congeneric methods do (e.g., local mean decomposition (LMD), empirical mode decomposition (EMD) and wavelet transform (WT) ).

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“…Yan et al., 30 Yi et al., 31 Zhang et al. 32 and Isham et al., 33 respectively, used the genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA) and differential evolution (DE) to determine the VMD parameters, and all of them achieved their respective goals.…”

Section: Introductionmentioning

confidence: 99%

“…Among these algorithms, differential evolution (DE) 33,34 is an efficient and powerful population-based global search technique with strong comprehensive convergence ability and robustness. Compared with GA and PSO, DE is simple and easy to implement, and it has more high optimisation efficiency.…”

Section: Introductionmentioning

confidence: 99%

Fault feature extraction of wind turbine gearbox under variable speed based on improved adaptive variational mode decomposition

Zheng

Wang

Qian

2019

Proceedings of the Institution of Mechanical Engineers, Part A:

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This paper proposes a study on gearbox fault feature extraction of wind turbine under variable speed condition using improved adaptive variational mode decomposition (VMD). Frequent changes in wind speed and critical noise interference make the vibration signal exhibit non-stationary characteristics. Although computational order tracking can transform non-stationary signals in the time domain into stationary angular signals, and then extract fault features from order spectrum obtained by FFT, the obtained order spectrum is liable to be polluted by noise or to appear order aliasing. To avoid order aliasing and eliminate noise interference, the original non-stationary signal is firstly processed using the improved adaptive VMD method called adaptive differential evolution – VMD (ADE-VMD). ADE-VMD can not only utilise the advantages of traditional VMD but also adaptively select narrow-band intrinsic mode function (NBIMF) to construct the reconstructed signal with less noise and without order aliasing. In the experiment, we compared the ADE-VMD method with other VMD methods such as GA-VMD, PSO-VMD and DE-VMD, and the results showed that ADE-VMD has excellent adaptive processing ability, and its convergence and optimisation speed are more remarkable. ADE-VMD can effectively filter the noise inference and avoid the order aliasing, so it is well suitable for fault feature extraction under variable speed.

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Intelligent wind turbine gearbox diagnosis using VMDEA and ELM

Cited by 33 publications

References 51 publications

A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox

A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

Fault feature extraction of wind turbine gearbox under variable speed based on improved adaptive variational mode decomposition

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