Publication

ANFIS and Takagi-Sugeno interval observers for fault diagnosis in bioprocess system

Journal Article (2024)

Journal

Journal of Process Control

Pages

103225

Volume

138

Doc link

https://doi.org/10.1016/j.jprocont.2024.103225

File

Download the digital copy of the doc pdf document

Authors

Abstract

This paper develops a data-driven approach for incipient fault diagnosis based on ANFIS and Takagi-Sugeno (TS) interval observers. First, the nonlinear bioreactor system is identified using an adaptive neuro-fuzzy inference system (ANFIS), which results in a set of polytopic TS models derived from measurement data. Second, a bank of TS interval observers is deployed to detect sensor and process faults using adaptive thresholds. Unlike other works that require training fault data, only fault-free data is considered for ANFIS learning in this work. Fault insolation is based on residual generation and evaluated on a fault signal matrix (FSM). Parametric uncertainty and measurement noise are considered to guarantee the method’s robustness. The effectiveness of the proposed method is tested on a well-known bioreactor Continuous stirred tank reactor system (CSTR) reference simulator.

Categories

automation.

Author keywords

Takagi-Sugeno observers, fault diagnosis, ANFIS, bioreactor

Scientific reference

E.d. Pérez, J. Fragoso-Mandujano, J. Guzmán-Rabasa, Y. González-Baldizón and S. Flores-Guirao. ANFIS and Takagi-Sugeno interval observers for fault diagnosis in bioprocess system. Journal of Process Control, 138: 103225, 2024.