Applied sensor fault detection and identification using hierarchical clustering and SOMNNs, with faulted-signal reconstruction

Zhang, Yu and Bingham, Chris and Yang, Zhijing and Gallimore, Michael and Stewart, Paul (2012) Applied sensor fault detection and identification using hierarchical clustering and SOMNNs, with faulted-signal reconstruction. In: 15th International Symposium on Mechatronics - MECHATRONIKA 2012, 5-7 December 2012, Prague, Czech Republic.

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Abstract

The paper presents two readily implementable and computationally efficient approaches for sensor fault detection and identification (SFD/I) for group of sensors in complex systems. Specifically, hierarchical clustering (HC) and self-organizing map neural networks (SOMNNs) are demonstrated for use on industrial turbine systems. HC fingerprints are found for normal operation, and SFD/I is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, for the SOMNNs, a fingerprint of the classification map is found during normal operation, and SFD/I is performed according to the classification changes from the outputs. Unlike most existing methods that only monitor the condition of a single sensor, here, the proposed methods are shown to detect sensor faults from a large group of sensors. Moreover, by comparison with other methods that require an additional algorithm to identify which sensor is faulted, here, the proposed methods are shown to achieve SFD/I in a single stage. Whilst the SOMNN provides a numerical classification of the sensor-group condition, dendrograms from the HC method provide a more useful graphical interpretation for SFD/I—the presented techniques are now fully operational and monitoring a fleet of industrial turbines in real-time. Moreover, it is also shown that, after identifying a faulted sensor, it is possible in some circumstances to reconstruct the measurements expected from that sensor (using the remaining non-faulted sensor information) and thereby facilitate improved unit availability. The efficacy of the proposed approaches is demonstrated through the copious use of experimental measurements.

Keywords:sensor fault detection and identification, self-organizing map neural network, hierarchical clustering, data reconstruction
Subjects:H Engineering > H730 Mechatronics
Divisions:College of Science > School of Engineering
ID Code:12548
Deposited On:20 Nov 2013 11:11

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