Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform

Teng, Wei; Ding, Xian; Cheng, Hao; Han, Chunyan; Liu, Yibing; Mu, Haihua

doi:10.1016/j.renene.2018.12.094

Cited by 97 publications

(37 citation statements)

References 24 publications

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“…He et al [ 14 ] proposed an adaptive redundant multiwavelet packet method to diagnose compound fault. Teng et al [ 15 ] established a novel vibration model and used empirical wavelet transform to find multiple fault feature. Lyu et al [ 16 ] proposed an improved maximum correlated kurtosis deconvolution method based on quantum genetic algorithm to diagnose compound fault.…”

Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

Tang

Tian

2020

Entropy

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Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC) is proposed. Firstly, the original signal was de-noised by SNDS which improved the noise reduction effect of singular difference spectrum by introducing negative entropy. Secondly, the de-noised signal was analyzed by fast spectral correlation. Finally, IFSC took the fourth-order energy as the index to determine the resonance band and separate the fault features of different single fault. The proposed method is applied to analyze the simulated compound signals and the experimental vibration signals, the results show that the proposed method has excellent performance in the separation of rolling bearing composite faults.

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Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

Tang

Tian

2020

Entropy

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“…4,5 Since the fault features of gearbox are often influenced and intertwined with each other under the non-stationary condition, the traditional shallow intelligent diagnosis models are difficult to detect and identify gearbox fault effectively with selected features according to prior knowledge. 6,7 Therefore, health monitoring and condition management of the gearbox has been one of the difficult problems that have plagued the engineering community during the past decades.…”

Section: Introductionmentioning

confidence: 99%

A novel fusion approach of deep convolution neural network with auto-encoder and its application in planetary gearbox fault diagnosis

Chen

Liu

Tang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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The fault features of gearbox are often influenced and interwoven with each other under the non-stationary condition. The traditional shallow intelligent diagnosis models are difficult to detect and identify gearbox faults with selected features according to prior knowledge. To solve this problem, a novel deep convolutional auto-encoding neural network is designed based on the fusion of the convolutional neural network with the automatic encoder in this research. The vibration signals of gearbox are transformed into Hilbert envelope spectrum by using Hilbert transform and Fourier transform, and the different characteristics of spectral spatial data are automatically learned by convolutional auto-encoding neural network with multiple convolution kernels. The parameters of the convolutional neural network are fine-tuned through a fully connected neural network with a small number of labeled samples. Through the analysis for gearbox fault experiments, the effectiveness and practicability of the proposed method in equipment fault diagnosis are verified. The deep convolutional neural network embedded in the auto-encoder has stronger learning ability, and the diagnosis performance is more stable and reliable in practical engineering application.

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“…Some studies on fault diagnosis for gearboxes have validated that the failure of any component in a drive train causes a particular variation in the performance. e source of a gearbox vibration can be reflected by signals of vibration behaviour [5,6,11] and can be captured and displayed by a particular expert system or procedure [12], so it is feasible to research the dynamic performance of a wind turbine drive train by analysing the different vibration signals, such as the time-domain spectrum and meshing frequency spectrum [13,14]. e results derived from various analytical methods and the relevant discussions indicated that many factors can influence the vibration of a wind turbine gearbox.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Parallel-Axis Helix Gear Pair on Wind Turbine Gearbox Vibration Control

Zhang

Chen³

et al. 2020

Shock and Vibration

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This article studies the effects of some basic parameters of a parallel-axis helix gear stage on wind turbine gearbox vibration in a case study: a multibody dynamic model is constructed to simulate the drive train of a faulted multistage wind turbine gearbox with serious vibrations. The significant vibration behaviour of the drive train for typical excitations is calculated, and the results according to specified geometric parameters of the gears are analysed in detail to investigate effective solutions for vibration reduction. The results indicate that the helix angle and numbers of teeth of a gear pair are the most significant factors for solving the problem. The effectiveness of the proposed solutions and relevant mechanisms are discussed and validated by a prototype vibration test.

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Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform

Cited by 97 publications

References 24 publications

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

A novel fusion approach of deep convolution neural network with auto-encoder and its application in planetary gearbox fault diagnosis

The Effect of Parallel-Axis Helix Gear Pair on Wind Turbine Gearbox Vibration Control

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