Fault isolation in analog circuits using a fuzzy inference system

El‐Gamal, Mohammed I.; Abdulghafour, M.

doi:10.1016/s0045-7906(01)00020-9

Cited by 26 publications

(15 citation statements)

References 24 publications

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“…Therefore, the application based on fuzzy logic theory in fault diagnostics is closer to human thinking habits and language expression. Fuzzy logic is an effective pattern recognition method, which has been successfully used in power [43], transmission line [44], transportation [45], and industrial production [46]. Fuzzy logic mainly imitates human's logical thinking and thus has strong capability of expressing knowledge.…”

Section: Fuzzy Logic and Neuro-fuzzy Systems (Nfss)mentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Section: Fuzzy Logic and Neuro-fuzzy Systems (Nfss)mentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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show abstract

“…Tarifa and Scenna [29] proposed a hybrid system that uses signed directed graphs (SDG) and fuzzy logic. El-Gamal and Abdulghafour [30] presented a fault isolation in analog circuits using a fuzzy inference system. Wo et al [31] presented an expert fault diagnostic system that uses rules with certainty factors.…”

Section: Expert Systems Approachmentioning

confidence: 99%

Hybrid computing techniques for fault detection and isolation, a review

Khoukhi

Khalid

2015

Computers & Electrical Engineering

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“…Due to several factors such as complexity of dynamics, incomplete uncertain knowledge and diverse sources of knowledge, many diagnostic methods and techniques are adopted in fault diagnosis research and development work. These methods and techniques can briefly be classified into the following: rule-based (Cho, Ahn, & Chung, 2003;El Gamal & Abdulghafour, 2003;Jämsä, Jounela, Vermasvuori, Endén, & Haavisto, 2003), knowledge-based (Cho et al, 2003;Ruiz et al, 2001), modelbased (Ding, Fennel, & Ding, 2004;Liu & Coghill, 2005), case-based (Cunningham, Smyth, & Bonzano, 1998), neural network (Mohamed, Abdelaziz, & Mostafa, 2005;Yang, Han, & An, 2004), rough set theory (Tay & Shen, 2003;Wang & Li, 2004), fuzzy logic (Dash, Rengaswamy, & Venkatasubramanian, 2003;Tarifa & Scenna, 2004) and statistical method (Yang, Lim, & Tan, 2005).…”

Section: Literature Reviewmentioning

confidence: 99%