Fault Detection and Diagnosis Using Cubature Kalman Filter for Nonlinear Process Systems

Authors

  • M. N. Ahmad Nazri Centre for Artificial Intelligence and Robotics, Malaysia – Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia.
  • Z. H. Ismail Centre for Artificial Intelligence and Robotics, Malaysia – Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia.
  • R. Yusof Centre for Artificial Intelligence and Robotics, Malaysia – Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia.

Keywords:

Cubature Kalman Filter, Bank of Residual, CSTR

Abstract

This paper presents a fault detection and diagnosis (FDD) for a nonlinear systems using multiple Cubature Kalman Filter (CKF) model. The proposed scheme able to identify sensors and actuators fault even with the presences of process and measurement noise. Comparison between actual faults with expected fault trajectory enables the FDD to narrow down possible scenario. The utilization of continuous stirred tank reactor (CSTR) simulation illustrates the performance of the scheme in nonlinear system. Result of the study shows the proposed method works effectively in determine the type of fault occurs in the CSTR.

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Published

2016-12-01

How to Cite

Ahmad Nazri, M. N., Ismail, Z. H., & Yusof, R. (2016). Fault Detection and Diagnosis Using Cubature Kalman Filter for Nonlinear Process Systems. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(11), 63–67. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1411

Issue

Vol. 8 No. 11: Artificial Intelligence and Robotics

Section

Articles

License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)