A Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks

• Main Authors: Hamidreza Mousavi, Mehdi Shahbazian, Nosrat Moradi
• Format: Article
• Language: English
• Published: Petroleum University of Technology 2017-01-01
• Series: Iranian Journal of Oil & Gas Science and Technology
• Subjects: sensor fault; fault isolation; reconstruction algorithm; auto-associative neural networks; multiple faults
• Online Access: http://ijogst.put.ac.ir/article_44384_1ce8d36de47de45f6ebd2dbe071373af.pdf

Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN). In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN) which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple fault conditions. The algorithm uses a calibration model based on AANN. AANN can reconstruct the faulty sensor using non-faulty sensors due to correlation between the process variables, and mean of the difference between reconstructed and original data determines which sensors are faulty. The algorithms are tested on a Dimerization process. The simulation results show that the S-AANN can isolate multiple faulty sensors with low computational time that make the algorithm appropriate candidate for online applications.