Sensor Fault Diagnosis Using an Algorithm Based on Auto-Associative Neural Networks

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.

Keywords:

Auto-Associative Neural Networks , Reconstruction Algorithm , Sensor fault diagnosis