A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF

Rezamand, Milad; Kordestani, Mojtaba; Carriveau, Rupp; Ting, David S.‐K.; Saif, Mehrdad

doi:10.1109/jsen.2019.2948997

Cited by 90 publications

(24 citation statements)

References 55 publications

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“…Due to the large magnitude of faults 1,2,4,6,7,8,12,13,14,17,18, the FDR values in each method are equal or close to 1. For faults 5,10,11,16,19,20, the proposed method has the better monitoring performance than other methods. A confusion matrix of the average monitoring result of faults (except 3, 9, 15) is shown in Table 4.…”

Section: A Tennessee Eastman Benchmark Processmentioning

confidence: 96%

Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description

2020

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This study proposes a multi-block fault detection method based on fault-sensitive slow features for large-scale dynamic industrial processes. Firstly, slow feature analysis (SFA) can effectively extract the process dynamic information. However, the slowest changing features may not contain more fault information. Thus, through the analysis of 2 T statistic in SFA-based process monitoring model, a fault sensitivity coefficient is defined as a new slow feature sorting criterion to select the most sensitive slow features to fault in each variable direction. Then, considering the unknown characteristics of the fault in the real-time monitoring process, the monitoring model is established for each dimension of variables based on the multi-block strategy. Finally, the support vector data description is used as a fusing method to integrate the statistics calculated in each sub-block to obtain an intuitive detection result. The effectiveness and superiority of the proposed strategy are demonstrated by the experiments on Tennessee Eastman benchmark process and an actual blast furnace ironmaking process.

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Section: A Tennessee Eastman Benchmark Processmentioning

confidence: 96%

Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description

2020

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“…Principal Component Analysis has been widely used in wind energy data analysis [24][25][26][27][28][29]. The rationale for this is that wind turbines are complex machines, which are regulated by a series of operation variables which are highly correlated among them selves but do not contain exactly the same information.…”

Section: Principal Component Analysismentioning

confidence: 99%

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

2021

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The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

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“…The errors of the reconstructed features were compared to a threshold developed with extreme value theory and was found to detect failure up to 10 hours quicker than a conventional neural network model, and had a comparable computational time. Rezamand et al [13] presented a technique using a wavelet probability distribution function (PDF) to detect incipient fault, with a regressional neural network used for data imputation. Recursive Principal Component Analysis (PCA) was used to extract features, and the Debauchie wavelet was used to extract the PDF, which was shown to decline close to failure.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

et al. 2020

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Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.

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A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF

Cited by 90 publications

References 55 publications

Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description

Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description

SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

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