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

IF 3.3 2区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS Journal of Process Control Pub Date : 2024-04-27 DOI:10.1016/j.jprocont.2024.103225
Esvan-Jesús Pérez-Pérez , José-Armando Fragoso-Mandujano , Julio-Alberto Guzmán-Rabasa , Yair González-Baldizón , Sheyla-Karina Flores-Guirao
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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.

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用于生物处理系统故障诊断的 ANFIS 和高木-菅野区间观测器
本文基于 ANFIS 和高木-菅野(TS)区间观测器,开发了一种数据驱动的初期故障诊断方法。首先,使用自适应神经模糊推理系统(ANFIS)识别非线性生物反应器系统,从而建立一套从测量数据中得出的多拓扑 TS 模型。其次,利用自适应阈值部署一组 TS 间隔观测器来检测传感器和流程故障。与其他需要训练故障数据的工作不同,本工作只考虑 ANFIS 学习的无故障数据。故障隔离基于残差生成,并在故障信号矩阵(FSM)上进行评估。为保证该方法的鲁棒性,考虑了参数不确定性和测量噪声。在著名的生物反应器连续搅拌罐反应器系统(CSTR)参考模拟器上测试了所提方法的有效性。
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来源期刊
Journal of Process Control
Journal of Process Control 工程技术-工程:化工
CiteScore
7.00
自引率
11.90%
发文量
159
审稿时长
74 days
期刊介绍: This international journal covers the application of control theory, operations research, computer science and engineering principles to the solution of process control problems. In addition to the traditional chemical processing and manufacturing applications, the scope of process control problems involves a wide range of applications that includes energy processes, nano-technology, systems biology, bio-medical engineering, pharmaceutical processing technology, energy storage and conversion, smart grid, and data analytics among others. Papers on the theory in these areas will also be accepted provided the theoretical contribution is aimed at the application and the development of process control techniques. Topics covered include: • Control applications• Process monitoring• Plant-wide control• Process control systems• Control techniques and algorithms• Process modelling and simulation• Design methods Advanced design methods exclude well established and widely studied traditional design techniques such as PID tuning and its many variants. Applications in fields such as control of automotive engines, machinery and robotics are not deemed suitable unless a clear motivation for the relevance to process control is provided.
期刊最新文献
Editorial Board Nonstationary incipient fault detection based on hybrid supervised trend-period variational autoencoder and its application in thermal power generation An ETF-based disturbance observer-based control for multivariable processes with time delays A continuous-time LPV models for a biofiltration process in wastewater nitrification — A global approach methodology for parametric estimation Kernel entropy quality correlation analysis for nonlinear industrial process fault detection
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