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

Mousavi, Hamidreza; Shahbazian, Mehdi

doi:10.22050/ijogst.2021.293463.1606

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

Document Type : Research Paper

Authors

Hamidreza Mousavi ¹

Mehdi Shahbazian ²

¹ M.S. Student, Department of Instrumentation & Automation Engineering, Petroleum University of Technology, Ahwaz, Iran

² Associate Professor, Department of Instrumentation & Automation Engineering, Petroleum University of Technology, Ahwaz, Iran

https://doi.org/10.22050/ijogst.2021.293463.1606

Abstract

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.

Highlights

An algorithm named improved auto-associative neural network (I-AANN) is presented for sensor fault diagnosis;
The algorithm uses a calibration model based on auto-associative neural network (AANN);
The I-AANN performance is compared with enhanced auto-associative neural network (E-AANN);
The algorithm is a kind of nonlinear principal component analysis (NLPCA);
The algorithm can detect and isolate faulty sensors via reconstruction;
Due to the low computational time, the algorithm is capable of online application.

Keywords

Auto-associative neural networks

Reconstruction algorithm

Sensor fault diagnosis

Subjects

Instrumentation & Automation in Oil Industries

Ali, E., and Al-humaizi, K., Advanced Control of Ethylene to Butene-1 Dimerization Reactor, Automatic Control and System Engineering, 2004.

Borgan, T. R., Condition Monitoring Based on “Black-box” and First Principal Models, M.S.Dissertation, Dept. Cyber. Eng. Norwegian University of Science and Technology, 2011.

Khaled, O., Fault Detection and Localization with Neural Principal Component Analysis, 18^thMediterranean Conference on Control & Automation Congress Palace Hotel, Marrakech, Morocco, June 23–25, 2010.

Kramer., M.A., Nonlinear Principal Component Analysis Using Auto-associative Neural Networks. AIChE Journal, 1991, DOI:10.1002/AIC.690370209

Najafi, Massieh (2003). Use of Auto-associative Neural Networks for Sensor Diagnostics. Master’s thesis, Texas A&M University, Texas A&M University, Available electronically from https://hdl.handle.net/1969.1/1392.

Najafi, M. Ch., Langari, Culp And R. Enhanced Auto-associative Neural Networks for Sensor Diagnostics (E-AANN), Proceedings of IEEE International Conference on Fuzzy Systems, 2004.

Sing, H., Development & Implementation of an Artificially Intelligent Search Algorithm for Sensor Fault Detection Using Neural Networks, M.S. dissertation, Dept. Mech. Eng., Texas A&M University, 2004.

Venkatasubramanian, E., Rengaswamy, R. Yin, K., Kavuri, S., N., A Review of Process Fault Detection and Diagnosis Part I: Quantitative Model-based Methods. Computers and Chemical Engineering, Vol. 27, p. 293–311, 2003.