Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6954861 | Mechanical Systems and Signal Processing | 2017 | 13 Pages |
Abstract
This paper presents a methodology for sensor fault diagnosis in nonlinear systems using a Mixture of Probabilistic Principal Component Analysis (MPPCA) models. This methodology separates the measurement space into several locally linear regions, each of which is associated with a Probabilistic PCA (PPCA) model. Using the transformation associated with each PPCA model, a parity relation scheme is used to construct a residual vector. Bayesian analysis of the residuals forms the basis for detection and isolation of sensor faults across the entire range of operation of the system. The resulting method is demonstrated in its application to sensor fault diagnosis of a fully instrumented HVAC system. The results show accurate detection of sensor faults under the assumption that a single sensor is faulty.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Reza Sharifi, Reza Langari,