An expert system for real-time fault diagnosis of complex chemical processes

Qian, Yu; Li, Xiuxi; Jiang, Yanrong; Wen, Yanqin

doi:10.1016/s0957-4174(02)00190-2

Cited by 69 publications

(31 citation statements)

References 2 publications

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“…Since September 2002, the knowledge maintenance module has been implemented in the expert system for real-time fault diagnosis of FCCU system developed earlier (Qian et al, 2003). During the applications of the expert system to FCCU, new knowledge and rules need to be added into the existing expert knowledge base from time to time, according to the changes of operating conditions and other circumstances.…”

Section: The Module In Application To the Industrymentioning

confidence: 99%

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Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation

Qian

Zheng

et al. 2005

Expert Systems with Applications

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Section: The Module In Application To the Industrymentioning

confidence: 99%

“…The authors developed earlier an expert system for fault diagnosis of process operations of the fluid catalytic cracking unit (Qian, Li, Jiang, & Wen, 2003). The fluid catalytic cracking unit (FCCU) is one of the most important production processes of the crude oil refining industry.…”

Section: Introductionmentioning

confidence: 99%

Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation

Qian

Zheng

et al. 2005

Expert Systems with Applications

Self Cite

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“…Thus, to develop an intelligent fault diagnosis system for elevator doors is imperative [4]. Many intelligent diagnostic systems have been employed to assist condition monitoring tasks by correctly interpreting the fault data, such as expert systems, artificial neural networks (ANNs), support vector machines and fuzzy logic systems, and the results of these techniques are promising [5][6][7]. However, many researches have shown that an individual decision system can only acquire a limited classification capability that is only appropriate for special data and may not be enough for a particular application.…”

Section: Introductionmentioning

confidence: 99%

Decision fusion system for fault diagnosis of elevator traction machine

et al. 2008

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Fault detection and diagnosis is critical for healthy operation of an elevator system. In order to realize a real-time and convenient diagnosis and satisfy the requirement of advanced maintenance of an elevator system, this paper proposes an intelligent fault diagnosis approach of induction motor for elevator traction machine using a developed decision fusion system. First, the basic knowledge of fusion techniques is briefly introduced which consists of classifier selection and decision fusion. Then a developed decision fusion system is presented. Next, four fusion algorithms-majority voting, Bayesian belief, multi-agent and modified Borda count-are employed for comparison in a real-world diagnosis experiment of a faulty elevator motor system. Based on the satisfactory results shown in the experiment, a big potential in real-world application is presented that is effective and cost saving only by analyzing stator current signals using proposed decision fusion system.

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“…Many intelligent diagnostic systems have been employed to assist condition monitoring tasks by correctly interpreting the fault data, such as expert systems, artificial neural networks (ANNs), support vector machines and fuzzy logic systems, and the results of these techniques are promising [4][5][6][7]. However, many researches have shown that when applying individual decision system with a single data source which can only acquire a limited classification capability and may be not enough for a particular application.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent decision fusion for motor fault diagnosis

Niu

Han

Yang

et al. 2007

Mechanical Systems and Signal Processing

126

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approach is required to generate better results. This paper proposes a decision fusion system for fault diagnosis, which integrates data sources of different types of sensors and decisions of multiple classifiers.First, non-commensurate sensors data sets are combined using an improved sensor fusion method at a decision-level by using relativity theory. The generated decision vectors are then selected based on correlation measure of classifiers in order to find an optimal sequence of classifiers fusion, which can lead to the best fusion performance. Finally, multi-agent classifiers fusion algorithm is employed as the core of the whole fault diagnosis system. The efficiency of the proposed system was demonstrated through fault diagnosis of induction motors. The experimental results show that this system can lead to super performance when compared with the best individual classifier with single source data.

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An expert system for real-time fault diagnosis of complex chemical processes

Cited by 69 publications

References 2 publications

Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation

Implementation of knowledge maintenance modules in an expert system for fault diagnosis of chemical process operation

Decision fusion system for fault diagnosis of elevator traction machine

Multi-agent decision fusion for motor fault diagnosis

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