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.
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering
Wang, X., Kruger, U., Irwin, G. W., McCullough, G., & McDowell, N. (2008). Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis. IEEE Transactions on Control Systems Technology, 16(1), 122-129. https://doi.org/10.1109/TCST.2007.899744