Applied sensor fault detection, identification and data reconstruction based on PCA and SOMNN for industrial systems

Zhang, Yu and Bingham, Chris and Gallimore, Michael and Yang, Zhijing and Stewart, Paul (2013) Applied sensor fault detection, identification and data reconstruction based on PCA and SOMNN for industrial systems. In: 12th WSEAS International Conference on Applications of Electrical Engineering (AEE '13), January 30th - February 1st 2013, Cambridge, Mass..

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Item Type:Conference or Workshop contribution (Paper)
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Abstract

The paper presents two readily implementable approaches for Sensor Fault Detection, Identification (SFD/I) and faulted sensor data reconstruction in complex systems, in real-time. Specifically, Principal Component Analysis (PCA) and Self-Organizing Map Neural Networks (SOMNNs) are demonstrated for use on industrial turbine systems. In the first approach, Squared Prediction Error (SPE) based on the PCA residual space is used for SFD. SPE contribution plot is employed for SFI. A missing value approach from an extension of PCA is applied for faulted sensor data reconstruction. In the second approach, SFD is performed by SOMNN based Estimation Error (EE), and SFI is achieved by EE contribution plot. Data reconstruction is based on an extension of the SOMNN algorithm. The results are compared in each examining stage. The validation of both approaches is demonstrated through experimental data during the commissioning of an industrial 15MW turbine.

Additional Information:RECENT ADVANCES IN ELECTRICAL AND COMPUTER ENGINEERING Proceedings of the 12th WSEAS International Conference on Applications of Electrical Engineering (AEE '13). Cambridge, Ma, January 30th - February 1st 2013
Keywords:Sensor fault detection and identification, Principal component analysis, Self-organizing map neural network, Data reconstruction
Subjects:G Mathematical and Computer Sciences > G120 Applied Mathematics
Divisions:College of Science > School of Engineering
ID Code:12551
Deposited On:15 Nov 2013 11:01

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