Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection

Hsu-Hao Yang, Mei-Ling Huang and Shih-Wei Yang
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Hsu-Hao Yang: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung City 41170, Taiwan
Mei-Ling Huang: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung City 41170, Taiwan
Shih-Wei Yang: Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung City 41170, Taiwan

Energies, 2015, vol. 8, issue 10, 1-16

Abstract: This paper presents a novel methodology to detect a set of more suitable attributes that may potentially contribute to emerging faults of a wind turbine. The set of attributes were selected from one-year historical data for analysis. The methodology uses the k -means clustering method to process outlier data and verifies the clustering results by comparing quartiles of boxplots, and applies the auto-associative neural networks to implement the residual approach that transforms the data to be approximately normally distributed. Hotelling T 2 multivariate quality control charts are constructed for monitoring the turbine’s performance and relative contribution of each attribute is calculated for the data points out of upper limits to determine the set of potential attributes. A case using the historical data and the alarm log is given and illustrates that our methodology has the advantage of detecting a set of susceptible attributes at the same time compared with only one independent attribute is monitored.

Keywords: wind energy; fault detection; auto-associative neural networks; hotelling T 2 control charts (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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