Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

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. •Data driven approach using a Mixture of Probabilistic Principal Component Analysis to nonlinear sensor fault diagnosis.•Sensor detectability index (SDI) based on the internal relationships of the system variables.•Application to multiple processes to experimentally validate approach.•Gaussian model found effective in representing noise.