Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic

Qu, Fuming; Liu, Jinhai; Zhu, Hongfei; Zhou, Bowen

doi:10.1016/j.apenergy.2019.114469

Cited by 52 publications

(17 citation statements)

References 37 publications

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“…If this crossed a threshold then failure was detected. Qu et al [14] presented a technique that utilised a combination of linguistic terms and errors produced by a neural network to produce fault factors, which could predict failure and their severity. Pei et al [15] presents a technique that uses K Nearest Neighbours (knn) to detect incipient failure in two turbines up to 6 months before 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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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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“…Zhang et al, presented a model to identify WT state parameters anomalies valid for condition parameters ranges fluctuating within the SCADA alarm threshold [15]. Qu et al proposed a WT fault diagnosis technique with SCADA data according to the expanded linguistic terms and rules through non-singleton fuzzy logic [16]. In [17], deep neural network (DNN)-based framework was considered to detect WT gearbox faults.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox

et al. 2021

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Wind turbines (WTs) are often operated in harsh and remote environments, thus making them more prone to faults and costly repairs. Additionally, the recent surge in wind farm installations have resulted in a dramatic increase in wind turbine data. Devising intelligent condition monitoring and fault warning systems are crucial to improving the efficiency and operation of wind farms and reducing maintenance costs. Gearbox is the major component that leads to turbine downtime. Its failures are mainly caused by the gearbox bearings. Devising condition monitoring approaches for the gearbox bearings is an effective predictive maintenance measure that can reduce downtime and cut maintenance cost. In this paper, we propose a hybrid intelligent condition monitoring and fault warning system for wind turbine's gearbox. The proposed framework encompasses the following: a) clustering filter-(based on power, rotor speed, blade pitch angle, and wind speed signals)-using the automatic clustering model and ant bee colony optimization algorithm (ABC), b) prediction of gearbox bearing temperature and lubrication oil temperature signals-using variational mode decomposition (VMD), group method of data handling (GMDH) network, and multi-verse optimization (MVO) algorithm, and c) anomaly detection based on the Mahalanobis distances and wavelet transform denoising approach. The proposed condition monitoring system was evaluated using 10 min average SCADA datasets of two 2 MW on-shore wind turbines located in the south of Sweden. The results showed that this strategy can diagnose potential anomalies prior to failure and inhibit reporting alarms in healthy operations.

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“…Somu et al [66] also performed trust centric ranking of web services by using the throughput and response time quality attributes. Based on the existing literature and understanding of the QoS attributes, it is appropriate to use throughput and response time as the most popular quality attributes because web services users mostly expect low response time and high throughput from service providers [67]. Therefore, the trustworthiness of a web service is more relevant to the performance evaluation of a web service, which is derived by using QoS attributes.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

et al. 2020

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To accurately rank various web services can be a very challenging task depending on the evaluation criteria used, however, it can play an important role in performing a better selection of web services afterward. This paper proposes an approach to evaluate trust prediction and confusion matrix to rank web services from throughput and response time. AdaBoostM1 and J48 classifiers are used as binary classifiers on a benchmark web services dataset. The trust score (TS) measuring method is proposed by using the confusion matrix to determine trust scores of all web services. Trust prediction is calculated using 5-Fold, 10-Fold, and 15-Fold cross-validation methods. The reported results showed that the web service 1 (WS1) was most trusted with (48.5294%) TS value, and web service 2 (WS2) was least trusted with (24.0196%) TS value by users. Correct prediction of trusted and untrusted users in web services invocation has improved the overall selection process in a pool of similar web services. Kappa statistics values are used for the evaluation of the proposed approach and for performance comparison of the two above-mentioned classifiers.

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Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic

Cited by 52 publications

References 37 publications

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

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

A Hybrid Intelligent Model for the Condition Monitoring and Diagnostics of Wind Turbines Gearbox

Evaluating Trust Prediction and Confusion Matrix Measures for Web Services Ranking

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