Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

Xun Wang, Uwe Kruger, George W. Irwin, Geoff McCullough, Neil McDowell

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)
2 Downloads (Pure)


This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.
Original languageEnglish
Pages (from-to)122-129
Number of pages8
JournalIEEE Transactions on Control Systems Technology
Issue number1
Publication statusPublished - Jan 2008

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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