A Self-reconstructing Algorithm for Single and Multiple-sensor Fault Isolation Based on Auto-associative Neural Networks
سال انتشار: 1396
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 313
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شناسه ملی سند علمی:
JR_IJOGST-6-1_006
تاریخ نمایه سازی: 18 اسفند 1397
چکیده مقاله:
Recently different approaches have been developed in the field of sensor fault diagnostics based onauto-associative neural network (AANN). In this paper, we present a novel algorithm called selfreconstructingauto-associative neural network (S-AANN) which is able to detect and isolate singlefaulty sensor via reconstruction. We have also extended the algorithm to be applicable to multiplefault conditions. The algorithm uses a calibration model based on AANN. AANN can reconstruct thefaulty sensor using non-faulty sensors due to correlation between the process variables, and the meanof the difference between the reconstructed and original data determines which sensors are faulty. Thealgorithms are tested on a dimerization process. The simulation results show that the S-AANN canisolate multiple faulty sensors at a low computational time, which makes the algorithm appropriatecandidate for online applications.
کلیدواژه ها:
نویسندگان
Hamidreza Mousavi
M.S. Student, Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran
Medi Shahbazian
Associate Professor, Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran
Nosra Moradi
۳System Engineer, Unit of Control and Instrumentation, Balal Platform, Iranian Offshore Oil Company, Lavan Island, Iran