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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.

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The purpose of this article is to investigate relative efficiency of management and variation of managerial efficiency among 37 domestic banks in Taiwan. The relative efficiency of management is analysed through Data Envelopment Analysis (DEA) to estimate the competitiveness of each bank and managerial efficiency is to show the efficiency variation of each bank through Malmquist index. This article also links those two types of efficiency by constructing a matrix of relative efficiency and managerial efficiency defining of eight different categories of banks. The empirical results show that all 37 banks possess an average relative efficiency value of 0.591, with a SD of 0.228. And there are 6 banks with an efficiency value of 1. From the results estimated by Malmquist model, it indicates there are 20 banks with an efficiency variation greater than 1. This means that managerial efficiency of those 20 banks has been improving. However, there are 17 banks with an efficiency variation less than 1. This means that managerial efficiency of those 17 banks has been declining

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