Sensor fault detection and diagnosis based on SOMNNs for steady-state and transient operation

Zhang, Yu, Bingham, Chris, Gallimore, Michael , Yang, Zhijing and Chen, Jun (2013) Sensor fault detection and diagnosis based on SOMNNs for steady-state and transient operation. In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), 15-17 July 2013, Milan, Italy.

Full content URL:

[img] PDF
Restricted to Repository staff only

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


The paper presents a readily implementable approach for sensor fault detection, identification (SFD/I) and faulted sensor data reconstruction in complex systems based on self-organizing map neural networks (SOMNNs). Two operational regimes are considered, i.e. the steady operation and operation with transients. For steady operation, SOMNN based estimation error (EE) are used for SFD. EE contribution plots are employed for SFI. For operation with transients, SOMNN classification maps are used for SFD/I comparing with the ‘fingerprint’ maps. In addition, extension algorithm of SOMNNs is developed for faulted sensor data reconstruction. The validation of the proposed approach is demonstrated through experimental data during the commissioning of industrial gas turbines.

Keywords:sensor fault detection, sensor fault identification, estimation error, self-organizing map neural network
Subjects:G Mathematical and Computer Sciences > G790 Artificial Intelligence not elsewhere classified
Divisions:College of Science > School of Engineering
Related URLs:
ID Code:12553
Deposited On:20 Nov 2013 11:37

Repository Staff Only: item control page