Wind Turbine Fault Diagnosis and Predictive Maintenance Through Statistical Process Control and Machine Learning

Abstract

This study applies statistical process control and machine learning techniques to diagnose wind turbine faults and predict maintenance needs by analyzing 2.8 million sensor data collected from 31 wind turbines from 2015 to 2017 in Taiwan. Unlike previous studies that only relied on historical wind turbine data, this study analyzed the sensor data with practitioners' insight by incorporating maintenance check list items into the data mining processes. We used Pareto analyses, scatter plots, and the cause and effect diagram to cluster and classify the failure types of wind turbines. In addition, control charts were used to establish a monitoring mechanism to track whether operation data are deviated from the controls (i.e., standard deviations) as a mean to detect wind turbine abnormalities. While statistical process control was applied to fault diagnosis, machine learning algorithms were used to predict maintenance needs of wind turbines. First, the density-based spatial clustering of applications with noise algorithm was used to classify abnormal-state wind turbine data from normal-state data. Then, random forest and decision tree algorithms were employed to construct the predictive models for wind turbine anomalies and tested with K-fold crossvalidation. The results indicate a high level of accuracy: 92.68% for the decision tree model, and 91.98% for the random forest model. The study demonstrates that, by data mining and modeling, the failures of wind turbines can be detected, and the maintenance needs of parts can be predicted. Model results may provide technicians early warnings, improve equipment efficient, and decrease system downtime of wind turbine operation. INDEX TERMS Decision trees, fault diagnosis, machine learning, predictive maintenance, random forest, statistical process control, wind energy.

Cost-Sensitive Extremely Randomized Trees Algorithm for Online Fault Detection of Wind Turbine Generators

“…Then, the parameters of different classifiers are selected based on grid optimization for each dataset. Finally, the experiment compares RF (Hsu et al, 2020;Jia et al, 2018) with XGBoost (Zhang et al, 2018), ERT (Janssens et al, 2016), CS-EDT (base classifier for CS-ERT), MetaCost (Kim et al, 2012), AdaCost (Yin et al, 2013), CSForest (Siers and Islam, 2015), and CS-ERT. To eliminate the contingency of the experiment, all methods use the 10-fold cross-validation method.…”

Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning

“…The authors in [64] proposed a new signal processing method for fault diagnosis of low-speed machin-ery based on DT approaches. In [65], the authors applied statistical process control and supervised ML techniques to diagnose wind turbine faults and predict maintenance needs. The researchers in [66] presented a semi-supervised ML method that uses the DT algorithm's co-training to handle unlabeled data and applied to fault classification in electric power systems.…”

Definition of the Future Skills Needs of Job Profiles in the Renewable Energy Sector

“…For that purpose, predictive maintenance is an ever-growing strategy within the renewable energy sector to minimize O&M (operations and maintenance) cost [33]. In addition to DT, AI, Big Data, machine learning, and IoT are used in the renewable energy sector to analyze and monitor real-time data gathered by sensors [33][34][35][36]. Modeling equipment behavior leads to early detection of damage, performance tracking, operation optimization, and the prevention of failures and inefficiencies [33].…”