Process supervision with the aid of fuzzy logic

Kiupel, N.; Frank, P.M.

doi:10.1109/icsmc.1993.384906

Cited by 16 publications

(6 citation statements)

References 11 publications

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“…Furthermore, a combination of analytical/ fuzzy model-based fault diagnosis concept is applied in [58] to the high-pressure-preheater line of a power plant, where the residuals generated from analytical knowledge of the plant is evaluated using fuzzy residual evaluation. The process of transforming quantitative knowledge (residuals) into qualitative knowledge (faulty symptoms) is covered in [59]. Also, in [91] the diagnosis of automotive faults was achieved by a combination of fuzzy estimators and FDI algorithms.…”

Section: Development Of a Hybrid Approach To Fdi Involves Determiningmentioning

confidence: 99%

The Application of Fuzzy Logic to the Diagnosis of Automotive Systems

Soliman

Krishnaswami

Rizzoni

1997

SAE Technical Paper Series

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Section: Development Of a Hybrid Approach To Fdi Involves Determiningmentioning

confidence: 99%

The Application of Fuzzy Logic to the Diagnosis of Automotive Systems

Soliman

Krishnaswami

Rizzoni

1997

SAE Technical Paper Series

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“…The residual evaluation in the case of purely quantitative model-based techniques is finally a comparison of the residual with a threshold. This bears the danger that an approaching 14 fault might not be detected, see also [7]. Using the fuzzy residual evaluation concept (FREC) provides the following advantages:…”

Section: Fuzzy-fault-symptom Generationmentioning

confidence: 99%

“…In this contribution attention is being focused on the method presented in [7]. The design of the FREC can be divided into three tasks.…”

Section: 2mentioning

confidence: 99%

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Fuzzy residual evaluation concept (FREC)

Kiupel

Köppen-Seliger

Kellinghaus

et al.

1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century

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In this paper we suggest a novel philosophy of process supervision based on knowledge based redundancy. The key idea is to replace the conventional residual evaluator of the fault diagnosis system based on crisp logic, by both a decision maker with fuzzy logic for residual evaluation and the human operator to make the final decisions using his natural intelligence, experience and common sense. The purpose of the employment of fuzzy logic for residual evaluation is to release only weighted alarms instead of yes-no decisions, such that (by definition) no false alarms can be produced; besides this the manmachine interaction becomes much easier. In contrast to the conventional expert system approach, the proposed concept leaves the final yes-no decisions to the natural intelligence, capability and responsibility of the human operator. In addition this method can be seen as an extension to the quantitative model-based techniques.Nevertheless the huge amount of information, which is normally given by most of the fault diagnosis schemes, should be both, filtered and reduced in the sense of the detectability and reliability of an FDI scheme. As an application example this concept has been applied to a part of a power plant in order to prove this theory.

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“…The fuzzy logic approach, unlike the logic threshold method, does not produce a simple fault/no-fault decision. It yields weighted alarms that can be used by a human operator to determine if a fault has occurred [56,57]. The development of a knowledgebase for fuzzy-based rules would allow a machine to replace the human operator and would provide the ability to perform real-time decision-making.…”

Section: Residual Evaluationmentioning

confidence: 99%

Diagnosis of operational changes in microelectromechanical systems via fault detection

Rittenhouse¹

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As microelectromechanical systems (MEMS) devices migrate into progressively more critical systems, the reliability these devices have to demonstrate must increasingly improve. Current research in the reliability of MEMS has succeeded in increasing the reliability of commercialized MEMS before they are integrated into an application by focusing on fabrication processes, device materials, and packaging. In this study, the focus is on assessing the reliability of MEMS during device operation. A fault detection approach was taken to diagnose incipient changes in the operation of MEMS devices. Model-based fault detection schemes utilizing the Kalman filter and H ∞ filter as residual generators were investigated. Analysis of the residual was conducted using a discrete Fourier transform (DFT). Two common MEMS research devices, the lateral comb resonator and parallel plate actuator, are used to demonstrate theoretically and experimentally the ability of the fault detectors to identify induced faults in linear and nonlinear domains of system operation. v 3.3.1 Extended Kalman Filter Results .

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Process supervision with the aid of fuzzy logic

Cited by 16 publications

References 11 publications

The Application of Fuzzy Logic to the Diagnosis of Automotive Systems

The Application of Fuzzy Logic to the Diagnosis of Automotive Systems

Fuzzy residual evaluation concept (FREC)

Diagnosis of operational changes in microelectromechanical systems via fault detection

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