%0 Journal Article %T Sensor Fault Diagnosis Using an Algorithm Based on Auto-Associative Neural Networks %J Iranian Journal of Oil and Gas Science and Technology %I Petroleum University of Technology %Z 2345-2412 %A Mousavi, Hamidreza %A Shahbazian, Mehdi %D 2021 %\ 10/01/2021 %V 10 %N 4 %P 14-30 %! Sensor Fault Diagnosis Using an Algorithm Based on Auto-Associative Neural Networks %K Auto-Associative Neural Networks %K Reconstruction Algorithm %K Sensor fault diagnosis %R 10.22050/ijogst.2021.293463.1606 %X Auto-associative neural network (AANN) has been recently used in sensor fault diagnosis. This paper introduces a new AANN based algorithm named improved AANN (I-AANN) for sensor single-fault diagnosis. An algorithm is a two-aimed approach that estimates the correct value of the faulty sensor by isolating the source of the fault. The performance of the algorithm is compared with the so-called enhanced AANN (E-AANN) in terms of computational time and fault reconstruction accuracy. The I-AANN has high performance, and it can isolate the source of fault quickly and accurately. A dimerization process model is used as a case study to examine and compare the performance of the algorithms. The results demonstrate that the I-AANN has superior performance. %U https://ijogst.put.ac.ir/article_139742_8b9e9d749c8f31fd58eef6b8781d7332.pdf