TY - JOUR ID - 44384 TI - A Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks JO - Iranian Journal of Oil and Gas Science and Technology JA - IJOGST LA - en SN - 2345-2412 AU - Mousavi, Hamidreza AU - Shahbazian, Mehdi AU - Moradi, Nosrat AD - M.S. Student, Department of Instrumentation & Automation Engineering, Petroleum University of Technology, Ahwaz, Iran. AD - Petroleum university of technology AD - Iranian Offshore Oil Company Y1 - 2017 PY - 2017 VL - 6 IS - 1 SP - 77 EP - 92 KW - Sensor Fault KW - Fault Isolation KW - Reconstruction Algorithm KW - Auto-Associative Neural Networks KW - multiple faults DO - 10.22050/ijogst.2017.44384 N2 - 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. UR - https://ijogst.put.ac.ir/article_44384.html L1 - https://ijogst.put.ac.ir/article_44384_1ce8d36de47de45f6ebd2dbe071373af.pdf ER -