NDLI: Fault diagnosis of a cooling package for AECS based on Principal Component Analysis

Content Provider	IEEE Xplore Digital Library
Author	Zhu Lei Geng Zhou
Copyright Year	2015
Description	Author affiliation: Sch. of Aeronaut. Sci. & Eng., Beihang Univ., Beijing, China (Zhu Lei; Geng Zhou)
Abstract	In this paper a method of Principal Component Analysis (PCA) is presented in doing the fault detection in the cooling package of Aircraft Environmental Control System (AECS). The cooling package is constituted of the heat exchanger and turbine. Due to the complexity of the fault pattern, the multitude of the data, and the relationships among so many data, the system fault is difficult to detect. In order to solve these problems, a PCA-based fault detection model is established to do the simulation research; by collecting the process data represented the fault characters, and using the PCA to analyze these process data. The simulation results show that the new algorithm is proved to be efficient; PCA has a good fault detection performance and a high sensitivity to predict the early fault. Besides, the fault can be located correctly by the PCA statistics and the contribution process diagram. Finally, some necessary sensors need to be added in the PCA and the AECS according to the schematic of aircraft air circulation system for fault detection and isolation. For example, temperature sensor and flow sensor can be added in the outlet of heat exchanger, and a rotation speed sensor can be added in the turbine cooler.
Starting Page	1
Ending Page	8
File Size	2002945
Page Count	8
File Format	PDF
e-ISBN	9781467385541
DOI	10.1109/PHM.2015.7380022
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Atmospheric modeling Heating Aircraft Environmental Control System Detection model Mathematical model MATLAB Artificial intelligence Aircraft Principal Component Analysis Principal component analysis
Content Type	Text
Resource Type	Article

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