Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

Qian, Yuning; Yan, Ruqiang

doi:10.1155/2016/6971952

Cited by 10 publications

(6 citation statements)

References 32 publications

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“…Du et al (2015) adopted sparse feature identification based on the union of redundant dictionaries to diagnose the wind turbine gearbox fault. Qian and Yan (2016) addressed the underdetermined blind source separation (UBSS) problem based on wavelet-assisted stationary subspace analysis and applied in to gearbox fault diagnosis in a wind turbine. Li et al (2019) proposed an UBSS algorithm to separate the single-channel signal in the hyperplane space with variational mode decomposition (VMD) and applied it to extract fault characteristics in the rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on kernel independent component analysis and antlion optimization

Zhong

Liu

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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Fault diagnosis of gearboxes based on vibration signal processing is challenging, as vibration signals collected by acceleration sensors are typically a nonlinear mixture of unknown signals. Furthermore, the number of source signals is usually larger than that of sensors because of the practical limitation on sensor positions. Hence, the fault characterization is actually a nonlinear underdetermined blind source separation (NUBSS) problem. In this paper, a novel NUBSS algorithm based on kernel independent component analysis (KICA) and antlion optimization (ALO) is proposed to address the technical challenge. The mathematical model demonstrates the nonlinear mixing of source signals in the underdetermined cases. Ensemble empirical mode decomposition is used as a preprocessing tool to decompose the observed signals into a set of intrinsic mode functions that suffers from the problem of redundant components. The correlation coefficient is utilized to eliminate the redundant components. An adaptive threshold singular value decomposition method is proposed to estimate the number of source signals. Then a whitening process is carried out to transform the overdetermined blind source separation (BSS) into determined BSS, which can be solved by the KICA method. However, the reasonable selection of parameters in KICA limits its application to some extent. Therefore, ALO and Fisher’s linear discriminant analysis are adopted to further enhance the accuracy of the KICA method. The separation performance of the proposed method is assessed through simulation. The numerical results show that the proposed method can accurately estimate the number of source signals and attains a higher separation quality in tackling nonlinear mixed signals when compared with the existing methods. Finally, the inner ring fault experiment is conducted to preliminarily validate the practicability of the proposed method in bearing fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis based on kernel independent component analysis and antlion optimization

Zhong

Liu

Wang

et al. 2021

Transactions of the Institute of Measurement and Control

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Youqing Wang. decomposition (EMD) [8]- [10], variational mode decomposition (VMD) [11]- [13], blind source separation [14]- [15], matching pursuit based methods [16]- [18], deep learning based methods [19]- [21]. However, these methods are unavailable for time-varying rotation speed operations.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

Wang

Xiang

2019

IEEE Access

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Conventional order tracking, which relies on a reference signal, is a common tool for rotary machinery fault diagnosis under speed fluctuation working conditions. However, it is inconvenient to install a speed sensor under certain circumstances. In this paper, we present a two-stage method to detect variable speed rolling bearing faults without a tachometer. In the first stage, the spline-kernelled chirplet transform (SCT) is applied to calculate the time-frequency distribution and extract the instantaneous rotation frequency. In the second stage, angle synchronous averaging is employed to resample the raw signal and detect fault features. The comparison investigations between SCT and other time-frequency analysis methods are performed by a numerical signal with a nonlinear instantaneous frequency. The experimental studies of variable speed rolling bearings with the outer and inner race faults are further performed. The results demonstrate that the proposed method can well detect rolling bearing faults without a key-phase signal from strong background noise under varying speed conditions. INDEX TERMS General parameterized time-frequency analysis methods, spline-kernelled chirplet transform, angle synchronous averaging, variable speed, bearing fault detection.

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“…Quantitative fault diagnosis, as the most common method for wind turbine fault diagnosis, includes the analytical model-based methods and the data-driven methods which are the main analytical methods. e method based on signal processing has been presented in [8,11,[16][17][18][19][20]. In [8], a complex wavelet transform is proposed for multifault detection using vibration signals collected from a real wind turbine.…”

Section: Introductionmentioning

confidence: 99%

“…However, the high computational costs and the noise pollution in the signal reconstruction by adding white noise increase the difficulty of fault diagnosis. A single senor-based blind source separation method presented in [16] has been utilized for fault diagnosis of the WTB. However, it neglects the effect of the time epoch number on the diagnostic results.…”

Section: Introductionmentioning

confidence: 99%

A CBA‐KELM‐Based Recognition Method for Fault Diagnosis of Wind Turbines with Time‐Domain Analysis and Multisensor Data Fusion

Long

Yang

Guo

et al. 2019

Shock and Vibration

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Fault diagnosis technology (FDT) is an effective tool to ensure stability and reliable operation in wind turbines. In this paper, a novel fault diagnosis methodology based on a cloud bat algorithm (CBA)-kernel extreme learning machines (KELM) approach for wind turbines is proposed via combination of the multisensor data fusion technique and time-domain analysis. First, the derived method calculates the time-domain indices of raw signals, and the fused time-domain indexes dataset are obtained by the multisensor data fusion. en, the CBA-based KELM recognition model that can identify fault patterns of a wind turbine gearbox (WTB) is automatically established with the fused dataset. e dataset includes a large number of samples involving 6 fault types under different operational conditions by 5 accelerometers. e effectiveness and feasibility of this proposed method are proved by adopting the datasets originated from the test rig, and it achieves a diagnostic accuracy of 96.25%. Finally, compared with the other peer-to-peer methods, the experimental classification results show that the proposed CBA-KELM technique has the best performances.

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Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

Cited by 10 publications

References 32 publications

Bearing fault diagnosis based on kernel independent component analysis and antlion optimization

Bearing fault diagnosis based on kernel independent component analysis and antlion optimization

A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

A CBA‐KELM‐Based Recognition Method for Fault Diagnosis of Wind Turbines with Time‐Domain Analysis and Multisensor Data Fusion

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