Principal component analysis applied to filtered signals for maintenance management

Márquez, Fausto Pedro García; García-Pardo, Isidro Peña

doi:10.1002/qre.1067

Cited by 55 publications

(26 citation statements)

References 14 publications

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“…The following four categories have been set in order to classify the obtained Pearson correlation coefficients: Weak correlation 0.3 ≤ | r | < 0.5 Moderate correlation: 0.5 ≤ | r | < 0.7 Strong correlation: | r | ≥ 0.7 Perfect correlation | r | = 1 Figure presents the correlations between the different variables. Each cell determines if the correlation is weak (w, yellow), moderate (m, green), strong (s, red), or perfect (p, blue).…”

Section: Proposed Approachmentioning

confidence: 99%

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Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

Marugán

Márquez

2019

Wind Energy

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Onshore and offshore wind farms require a high level of advanced maintenance. Supervisory control and data acquisition (SCADA) and condition monitoring systems are now being employed, generating large amounts of data. They require robust and flexible approaches to convert dataset into useful information. This paper presents a novel approach based on the correlations of SCADA variables to detect and identify faults and false alarms in wind turbines. A correlation matrix between all the SCADA variables is used for pattern recognition. A new method based on curve fittings is employed for detecting false alarms and abnormal behaviours or faults in the components. The study is done in a real case study, validated with false alarms.

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Section: Proposed Approachmentioning

confidence: 99%

“…The following four categories have been set in order to classify the obtained Pearson correlation coefficients [54][55][56] :…”

Section: The Variables Inmentioning

confidence: 99%

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

Marugán

Márquez

2019

Wind Energy

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“…The ultrasonic signal studied should be conditioned and denoised to train the classifiers properly [45]. The wavelet transform has been used in this paper to perform signal denoising [46,47].…”

Section: Approach For Delamination Detection and Diagnosis In Wtbmentioning

confidence: 99%

Machine Learning for Wind Turbine Blades Maintenance Management

2017

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Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule-Walker model is employed for feature extraction, and Akaike's information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F-score.

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“…This leads to the wavelet decomposition trees ( Figure 5) [1,9]. Additional information is obtained by filtering at each level.…”

Section: Wavelet Pre-filter and De-noisingmentioning

confidence: 99%

A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers

Muñoz¹,

Márquez²,

Jiménez³

et al. 2017

EiN

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Science and Technology article citation info: IntroductionNon-destructive testing (NDT) for fault detection in structures has received more attention in recent years. Significant advances in instrumentation technology and digital signal processing have been made [16,17,19,25,30]. Signal processing methods together with structural health monitoring (SHM) permit the identification and diagnosis of faults and their location based on changes in static and

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Principal component analysis applied to filtered signals for maintenance management

Cited by 55 publications

References 14 publications

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

Advanced analytics for detection and diagnosis of false alarms and faults: A real case study

Machine Learning for Wind Turbine Blades Maintenance Management

A heuristic method for detecting and locating faults employing electromagnetic acoustic transducers

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