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Sensors 2016, 16(5), 623; doi:10.3390/s16050623

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

Department of Automation Measurement and Control, Harbin Institute of Technology, Harbin 150001, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 27 February 2016 / Revised: 6 April 2016 / Accepted: 14 April 2016 / Published: 29 April 2016
(This article belongs to the Section Physical Sensors)
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Abstract

In a complex system, condition monitoring (CM) can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor) to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR). The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA) Ames Research Center and have been used as Prognostics and Health Management (PHM) challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved. View Full-Text
Keywords: condition monitoring; sensor selection; anomaly detection; mutual information; Gaussian Process Regression condition monitoring; sensor selection; anomaly detection; mutual information; Gaussian Process Regression
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MDPI and ACS Style

Liu, L.; Liu, D.; Zhang, Y.; Peng, Y. Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines. Sensors 2016, 16, 623.

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