Fault Prognosis and Remaining Useful Life Prediction of Wind Turbine Gearboxes Using Current Signal Analysis

Cheng, Fangzhou; Qu, Liyan; Wang, Qiao

doi:10.1109/tste.2017.2719626

Cited by 142 publications

(77 citation statements)

References 48 publications

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“…According to the literature [21,55], the presence of this third harmonic pair confirms rotor unbalance of mechanical nature. It can be appreciated that the number of f RFS and f GBX peaks evolves in parallel, which might be due to the fact that f RFS peaks appear in the case of mechanical asymmetries and scoring or debris (related to f GBX components), are purely mechanical faults.…”

mentioning

confidence: 66%

“…Generator bearing and eccentricity, as well as pinion gear faults, can automatically be detected through stator current analysis using information entropy [20], through an adaptive neuro-fuzzy inference system and particle filtering [21] or computing the energy from the fault-related frequencies [22,23]. The results are validated at laboratory benches for the studies in [20][21][22][23] and using simulated data in [24,25].…”

Section: Fault Diagnosis Techniques For Drive Trains and Induction Gementioning

confidence: 99%

“…Nevertheless, the most common approach for diagnosing a faulty gearbox is to calculate the frequency components that each shaft and gear would produce in the presence of a fault [21,22,26,46,48,49], which is given by Equation (4a) for shaft-related faults and Equation (4b) for gear-mesh-related faults:…”

Section: Current Signature Analysismentioning

confidence: 99%

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Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Artigao

Koukoura

Carroll³

et al. 2018

Energies

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Abstract:The goal of the present paper is to achieve the diagnosis of an in-service 1.5 MW wind turbine equipped with a doubly-fed induction generator through current signature and vibration analyses. Real data from operating machines have rarely been analysed in the scientific literature through current signature analysis supported by vibrations. The wind turbine under study was originally misdiagnosed by the operator, where a healthy component was replaced and the actual failure continued progressing. The chronological evolution of both the electrical current and vibration spectra is presented to conduct an in-depth tracking of the fault. The diagnosis is achieved through spectral analysis of the stator currents, where fault frequency components related to rotor mechanical unbalance are identified. This is confirmed by the vibration analysis, which provides insightful information on the health of the drive train. These results can be implemented in condition monitoring strategies, which is of great interest to optimise operation and maintenance costs of wind farms.

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confidence: 66%

Section: Fault Diagnosis Techniques For Drive Trains and Induction Gementioning

confidence: 99%

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Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Artigao

Koukoura

Carroll³

et al. 2018

Energies

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“…The gearbox has one of the highest downtimes and replacement costs out of all wind turbine components . Consequently, trying to predict gearbox failure has been the focus of a number of past papers . Amongst techniques to predict gearbox failure, there are both model‐based and data‐driven approaches.…”

Section: Introductionmentioning

confidence: 99%

“…11 Consequently, trying to predict gearbox failure has been the focus of a number of past papers. 4,6,10,[12][13][14][15][16][17][18][19][20][21] Amongst techniques to predict gearbox failure, there are both model-based 13,17 and data-driven 4,7,8,19,22 approaches. In the past, model-based approaches have utilised a nonlinear model of the system 13 or a temperature model of the gearbox.…”

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confidence: 99%

Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

et al. 2018

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This research investigates the prediction of failure and remaining useful life (RUL) of gearboxes for modern multi‐megawatt wind turbines. Failure and RUL are predicted through the use of machine learning techniques and large amounts of labelled wind turbine supervisory control and data acquisition (SCADA) and vibration data. The novelty of this work stems from unprecedented access to one of the world's largest wind turbine operational and reliability databases, containing thousands of turbine gearbox failure examples and complete SCADA and vibration data in the build up to those failures. Through access to that data, this paper is unique in having enough failure examples and data to draw the conclusions detailed in the remainder of this abstract. This paper shows that artificial neural networks provide the most accurate failure and RUL prediction out of three machine learning techniques trialled. This work also demonstrates that SCADA data can be used to predict failure up to a month before it occurs, and high frequency vibration data can be used to extend that accurate prediction capability to 5 to 6 months before failure. This paper demonstrates that two class neural networks can correctly predict gearbox failures between 72.5% and 75% of the time depending on the failure mode when trained with SCADA data and 100% of the time when trained with vibration data. Data trends in the build up to failure and weighting of the SCADA data inputs are also provided. Lastly, this work shows how multi‐class neural networks demonstrate more potential in predicting gearbox failure when trained with vibration data as opposed to training with SCADA data.

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Information entropy theory for steam turbine system monitoring study

Cui

et al. 2020

Engineering Reports

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A steam turbine is one of the components in a power plant. Considered as a strong coupling system, steam turbines may possess correlation information among the operation parameters. In this article, a correlation extraction method based on mutual information theory is proposed for performance monitoring. First, five functions between two variables including linear and nonlinear relationships are tested to validate the proposed method. In the first industrial case study, a sensor fault detection based on the governing stage pressure is conducted on the abnormal fluctuation mutual entropy values calculated by operation data. The results show the proposed method may effectively be used to detect sensor errors and locate the first error sample. Furthermore, uncertainty analysis on the heat rate is studied to sort the importance degree of the relative parameters. The mutual information entropy difference values are then calculated to quantify the sensitivity degree of the operation parameters to the heat rate index. The results in this case study are in accordance with the sequences by the Guide to the Expression of Uncertainty Measurement (GUM) and Monte Carlo (MC) methods. In conclusion, mutual information entropy may hence be used as a new solution for turbine monitoring, combined with information theory.

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Fault Prognosis and Remaining Useful Life Prediction of Wind Turbine Gearboxes Using Current Signal Analysis

Cited by 142 publications

References 48 publications

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Current Signature and Vibration Analyses to Diagnose an In-Service Wind Turbine Drive Train

Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

Information entropy theory for steam turbine system monitoring study

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