Nonparametric and semi-parametric sensor recovery in multichannel condition monitoring systems

Haitao Liao, Jian Sun

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish (US)
Article number5934386
Pages (from-to)744-753
Number of pages10
JournalIEEE Transactions on Automation Science and Engineering
Issue number4
StatePublished - Oct 2011


  • Asynchronous data
  • condition monitoring (CM)
  • functional principal component analysis (FPCA)
  • sensor recovery

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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