Integrating Fuzzy based Fault diagnosis with Constrained Model Predictive Control for Industrial Applications

Mani, Geetha; Sivaraman, Natarajan

doi:10.5370/jeet.2017.12.2.886

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Post detection of fault occurrences, they controlled motor drive parameters (motor speed and current) to recover the system to its healthy state. Mani and Sivaraman [26] employed a fuzzy decision-making system to classify fault types. Based on predefined fault detection rules, their proposed fuzzy classifier computed output variables to classify the current system state as healthy or faulty.…”

Section: Introductionmentioning

confidence: 99%

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Baek

2021

Int J Adv Manuf Technol

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As automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.

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Section: Introductionmentioning

confidence: 99%

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

Baek

2021

Int J Adv Manuf Technol

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“…Concerning this problem, Japanese scholars Takagi and Sugeno put forward T-S (Takagi-Sugeno) fuzzy models for description of nonlinear systems in 1985 with a huge progress in the studies of fuzzy control of nonlinear systems [4]. Moreover, fuzzy control has been widely used so far [5]- [15]. Aiming at the problems existing in the current energy management system (EMS), the fuzzy plus filter energy management controller is designed by [7], and the improved dual-object optimization problem of fuzzy EMS is settled by the improved genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

2D Fuzzy Constrained Fault-Tolerant Predictive Control of Nonlinear Batch Processes

Luo

Wang

2019

IEEE Access

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Concerning actuator gain faults and strong system nonlinearity in batch processes, a design method of a kind of 2D fuzzy constrained model fault-tolerant predictive controller is proposed. Firstly, introduce two errors: state error and output error, after that the original system model is converted into a 2D Roesser fault model. Meanwhile, the design of iterative learning fault tolerant control under constraints has been transformed into the determination of the constrained update law. Subsequently, real-time on-line design of the fuzzy fault-tolerant update law that ensures the closed-loop system robustly asymptotic stability is presented by taking the appearance of linear matrix inequalities (LMIs) with constraint subject to the designed infinite optimization performance index and Lyapunov stability theory. Finally, taking a three-tank case as an example to compare with the 1D of the method proposed in this paper, the comparison results illustrated the 2D method of this paper has better control effects. INDEX TERMS Actuator faults, fuzzy constrained iterative learning predictive fault-tolerant control, nonlinear batch processes, 2D T-S fuzzy model.

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Integrating Fuzzy based Fault diagnosis with Constrained Model Predictive Control for Industrial Applications

Cited by 2 publications

References 6 publications

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems

2D Fuzzy Constrained Fault-Tolerant Predictive Control of Nonlinear Batch Processes

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