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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.



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





Autor: Daqi Zhu 1,* , Jie Bai 1 and Simon X. Yang 2

Fuente: http://mdpi.com/



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