Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method
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TLDR
The diagnostic results show that the proposed method, called the empirical wavelet thresholding, can be an effective tool to diagnose naturally damaged large-scale wind turbine blade bearings.About:
This article is published in Renewable Energy.The article was published on 2020-02-01 and is currently open access. It has received 80 citations till now. The article focuses on the topics: Bearing (mechanical) & Turbine blade.read more
Citations
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Journal ArticleDOI
A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings
Zepeng Liu,Long Zhang +1 more
TL;DR: This paper aims at systematically and comprehensively summarizing current large-scale wind turbine bearing failure modes and condition monitoring and fault diagnosis achievements, followed by a brief summary of future research directions for wind turbine Bearing fault diagnosis.
Journal ArticleDOI
Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis
TL;DR: The diagnostic framework combining DRS-CEL and morphological analysis is validated by comparing several methods and related studies, which offers a promising solution for wind-farm applications.
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Failure prediction, monitoring and diagnosis methods for slewing bearings of large-scale wind turbine: A review
TL;DR: Current situation of researches on wind turbine slewing bearing is summarized systematically and failure prediction, monitoring and diagnosis methods of slewing bearings for industries are reviewed and summarized, which can be potentially used for wind energy industry.
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Multichannel fault diagnosis of wind turbine driving system using multivariate singular spectrum decomposition and improved Kolmogorov complexity
TL;DR: A new approach based on multivariate singular spectrum decomposition (MSSD), an improved complexity metric abbreviated as IKC is proposed to capture the fault information of multichannel mode components, which can enhance fault feature extraction ability of KC.
Journal ArticleDOI
Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM
Fengqian Zou,Haifeng Zhang,Haifeng Zhang,Sang Shengtian,Li Xiaoming,Wanying He,Xiaowei Liu,Xiaowei Liu +7 more
TL;DR: A method that combines multi-scale weighted entropy morphological filtering (MWEMF) signal processing and bidirectional long-short term memory neural networks (Bi-LSTM) is proposed to overcome the disadvantages of lacking intrinsic mode function (IMF) modal aliasing, low degree of discrimination between data of different fault types, high computational complexity.
References
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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches
TL;DR: The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade.
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Empirical Wavelet Transform
TL;DR: This paper presents a new approach to build adaptive wavelets, the main idea is to extract the different modes of a signal by designing an appropriate wavelet filter bank, which leads to a new wavelet transform, called the empirical wavelets transform.
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Fast computation of the kurtogram for the detection of transient faults
TL;DR: This communication describes a fast algorithm for computing the kurtogram over a grid that finely samples the ( f, Δ f ) plane and the efficiency of the algorithm is illustrated on several industrial cases concerned with the detection of incipient transient faults.
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Computing the discrete-time "analytic" signal via FFT
TL;DR: Starting with a real-valued N-point discrete-time signal, frequency-domain algorithms are provided for computing the complex-valued standard N- point discrete time 'analytic' signal of the same sample rate.
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Kurtosis: A Critical Review
TL;DR: The concept of kurtosis has been formally defined in this paper as the movement of probability mass from the shoulders of a distribution into its center and tails, and it can be formalized in many ways.
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