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Sensors 2010, 10(1), 241-253; doi:10.3390/s100100241

A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis

1
Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Shanghai, 200135, China
2
The Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON. N1G 2W1, Canada
*
Author to whom correspondence should be addressed.
Received: 10 October 2009 / Revised: 30 November 2009 / Accepted: 4 December 2009 / Published: 29 December 2009
(This article belongs to the Section Chemical Sensors)
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Abstract

A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time. View Full-Text
Keywords: multi-fault diagnosis; principal component analysis; signal reconstruction; fault detection; fault isolation multi-fault diagnosis; principal component analysis; signal reconstruction; fault detection; fault isolation
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhu, D.; Bai, J.; Yang, S.X. A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis. Sensors 2010, 10, 241-253.

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