NDLI: Nonparametric and Semi-Parametric Sensor Recovery in Multichannel Condition Monitoring Systems

Content Provider	IEEE Xplore Digital Library
Author	Haitao Liao Jian Sun
Copyright Year	2004
Abstract	Condition monitoring (CM) has been recognized as a more effective failure prevention paradigm than the time-based counterpart. CM can be performed via an array of sensors providing multiple, real-time equipment degradation information with broad coverage. However, loss of sensor readings due to sensor abnormalities and/or malfunction of connectors has long been a hurdle to reliable fault diagnosis and prognosis in multichannel CM systems. The problem becomes more challenging when the sensor channels are not synchronized because of different sampling rates used and/or time-varying operational schemes. This paper provides a nonparametric sensor recovery technique and a semi-parametric alternative to enhance the robustness of multichannel CM systems. Based on historical data, models for all the sensor signals are constructed using functional principal component analysis (FPCA), and functional regression (FR) models are developed for those correlated signals. These models with parameters updated in online implementation can be used to recover the lost sensor signals. A case study of aircraft engines is used to demonstrate the capability of the proposed approaches. In addition to recovering asynchronous sensor signals, the proposed approaches are also compared with the Elman neural network as a popular alternative in recovering synchronous sensor signals.
Sponsorship	IEEE Robotics and Automation Society
Starting Page	744
Ending Page	753
Page Count	10
File Size	1280284
File Format	PDF
ISSN	15455955
Volume Number	8
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Condition monitoring Robustness Autoregressive processes Data models Artificial neural networks Degradation Principal component analysis sensor recovery Asynchronous data condition monitoring (CM) functional principal component analysis (FPCA)
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering

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