Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models Academic Article

abstract

• 2016 Elsevier Ltd 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.

authors

• Langari, Gholamreza

published proceedings

• MECHANICAL SYSTEMS AND SIGNAL PROCESSING

author list (cited authors)

• Sharifi, R., & Langari, R.

citation count

• 51

complete list of authors

• Sharifi, Reza||Langari, Reza

publication date

• January 2017

publisher

• Elsevier  Publisher

published in

• Mechanical Systems and Signal Processing  Journal

keywords

• Fault Diagnosis
• Mixture Of Probabilistic Pca
• Nonlinear Fault Detection
• Principal Component Analysis
• Sensor Fault Detection

Digital Object Identifier (DOI)

• 10.1016/j.ymssp.2016.08.028

start page

• 638

end page

• 650

volume

• 85

URL

• http://dx.doi.org/10.1016/j.ymssp.2016.08.028