Wind turbine fault detection and classification by means of image texture analysis

Ruiz, Magda; Mujica, Luis Eduardo; Alférez, Santiago; Acho, Leonardo; Tutivén, Christian; Vidal, Yolanda; Rodellar, José; Pozo, Francesc

doi:10.1016/j.ymssp.2017.12.035

Cited by 87 publications

(49 citation statements)

References 37 publications

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“…These faults are inspired by research in both proprietary and public domain sources [23]. As an extra reference, the interested reader can find a comprehensive description of these faults and their importance in [24]. The stochastic, full-field, turbulent-wind simulator TurbSim-developed by NREL-was used to generate the wind velocity fields applied in the simulations.…”

Section: Model Overviewmentioning

confidence: 99%

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Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

2018

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Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.

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Section: Model Overviewmentioning

confidence: 99%

“…From Equations (22) and (24), it is obvious that optimization depends only on dot products of pairs of samples. Additionally, the decision rule depends only on the dot product.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

2018

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“…For rotating machinery, Park et al employed transmission errors and ensemble empirical mode decomposition to detect gear teeth spalls and cracks as anomalies. Ruiz et al analyzed image textures generated from time domain signals to detect and classify multiple wind turbine faults. Kullaa used experimental multichannel vibration measurements to conduct sensor validation based on the minimum mean square error estimation.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

Tang

Chen

Bao

et al. 2018

Struct Control Health Monit

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Structural health monitoring (SHM) is used worldwide for managing and maintaining civil infrastructures. SHM systems have produced huge amounts of data, but the effective monitoring, mining, and utilization of this data still need in-depth study. SHM data generally includes multiple types of anomalies caused by sensor faults or system malfunctions that can disturb structural analysis and assessment. In the routine data pre-processing, multiple signal processing techniques are required to detect the anomalies, respectively, which is inefficient. The large variations of extracted features from massive SHM data make the data anomaly detection techniques prone to be over-processed or under-processed. Even with expert intervention, the parameter tuning, associated with multiple data preprocessing methods, is still a challenge, making the procedure expensive and inefficient. In addition, one data anomaly detection technique frequently mis-detects other types of anomaly. In this work, we focus on the anomaly detection in the stage of data pre-processing that little work has been done based on the real-world continuous SHM data with multiclass anomalies. We proposed a novel data anomaly detection method based on a convolutional neural network (CNN) that imitates human vision and decision making. First, we split raw time series data into sections, and visualized the data in time and frequency domain, respectively. Then each section's images were stacked as a single dual-channel image and labeled according to graphical features (multi-2D image space expression). Second, a CNN was designed and trained for data anomaly classification, during which the descriptions and representations of the anomalies' features were learned by convolution. To validate our work, we considered the effects of balanced and imbalanced training sets and training ratios on actual acceleration data from an SHM system for a long-span cable-stayed bridge. The results show that our method could detect the multipattern anomalies of SHM data efficiently with high accuracy. The proposed dual-information CNN-based design makes this detection process readily scalable, faster, and more accurate, thereby providing a novel perspective with strong potential for SHM data preprocessing. KEYWORDScomputer vision, convolutional neural network (CNN), data anomaly detection, long-span bridge, structural health monitoring (SHM) | INTRODUCTIONStructural health monitoring (SHM) is used worldwide to manage and maintain civil infrastructure systems by evaluating their structural loads, responses, and real-time performance, and by predicting the future behavior of structures of all types. 1-4 SHM has produced huge amounts of data. For example, the SHM system used for the Sutong Bridge in China, which has 785 sensors, produces 2.5 TB of data each year. The effective mining and utilization of SHM data is an important topic that needs in-depth study. However, researchers have faced challenges because the SHM data generally include multiple types of anomalies caused by sensor fa...

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“…A nonlinear data‐driven approach is proposed to identify anomalies of WT gearbox bearings and generator windings . Furthermore, a great deal of interest is now focusing on fault detection by using modern techniques, including image texture analyses, adaptive algorithms, deep learning, and other methods. …”

Section: Introductionmentioning

confidence: 99%

Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing

Fan

Jia³

et al. 2019

Wind Energy

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Under the framework of normal behavior modeling, this paper develops a novel scheme for fault detection via quantile regression neural networks (QRNNs). The QRNN model is a combination of quantile regressions and neural networks. It is able to identify the normal status or extract the normal behavior data accurately and quickly through lower and upper regression quantiles.Additionally, it is flexible to explore the potential nonlinear patterns contained in the normal status by taking advantage of neural networks. Finally, we monitor the residuals produced from QRNN to detect faults by using the exponentially weighted moving average (EWMA) control chart. The utility of our scheme is illustrated by empirical analyses of bearing fault detection based on Supervisory Control and Data Acquisition (SCADA) data from a wind turbine. We find that the QRNN model outperforms the multiple linear regression (MLR) and back propagation neural networks (BPNNs) in terms of mean absolute error (MAE). Besides, the obtained relationship between the width of control limits in EWMA and the number of alarms provides an important and convenient way for practical applications. KEYWORDS exponentially weighted moving average, fault detection, normal behavior modeling, quantile regression neural networks, wind turbine INTRODUCTIONWind energy is one of the most significant clean renewable energies for electrical generation. An increasing number of wind farms have been built to fulfill human's growing power demand. However, wind turbines (WTs) are typically deployed in a remote or offshore location with abundant wind energy, where performing regular inspections to maintain normal operations will cost a lot. In addition, unscheduled and reactive maintenances could increase the meaningless downtime, causing the loss of revenue. Therefore, how to perform more effective operations and maintenance procedures via monitoring remotely with lower cost has attracted more and more attentions from both academics and practitioners.Condition monitoring systems (CMSs) require not only high-level knowledge about the systems but also the high-cost vibration sensors, which hinders from the prevalence of CMSs. 1-3 On the contrary, the data collected via Supervisory Control and Data Acquisition (SCADA) systems that archive useful information in a convenient manner is universally available. Extensive efforts have been devoted to developing various methods for fault detection of WTs based on SCADA data.Based on SCADA data, the residuals produced from three models including a generalized mapping regressor (GMR), a general regression neural network (GRNN), and a feed-forward multilayer perceptron (MLP) have already been used to monitor the power curve of WTs. 4 The copula approach, estimating bivariate probability distribution function, is proposed to model the power curve so that the deviation between the existing and expected behavior can be checked. 5 The Mahalanobis distance is applied to detect faults in the optional curves containing the power curve, rotor curv...

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Wind turbine fault detection and classification by means of image texture analysis

Cited by 87 publications

References 37 publications

Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring

Quantile regression neural network‐based fault detection scheme for wind turbines with application to monitoring a bearing

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