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Uncertain fault diagnosis problem using neuro-fuzzy approach and probabilistic model for manufacturing systems

Applied intelligence (Dordrecht, Netherlands), 2018-09, Vol.48 (9), p.3143-3160 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2018 ;Applied Intelligence is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0924-669X ;EISSN: 1573-7497 ;DOI: 10.1007/s10489-017-1132-8

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  • Title:
    Uncertain fault diagnosis problem using neuro-fuzzy approach and probabilistic model for manufacturing systems
  • Author: Djelloul, Imene ; Sari, Zaki ; Latreche, Khaled
  • Subjects: Artificial Intelligence ; Artificial neural networks ; Bayesian analysis ; Classification ; Computer Science ; Fault detection ; Fault diagnosis ; Faults ; Fuzzy logic ; Fuzzy systems ; Knowledge bases (artificial intelligence) ; Machine learning ; Machines ; Manufacturing ; Mechanical Engineering ; Milk ; Model accuracy ; Multilayer perceptrons ; Neural networks ; Pasteurization ; Probabilistic models ; Processes ; Variation
  • Is Part Of: Applied intelligence (Dordrecht, Netherlands), 2018-09, Vol.48 (9), p.3143-3160
  • Description: This paper is concerned with fault detection and diagnosis problem in manufacturing systems. In such industrial environment, production systems are subject to several faults caused by a number of factors including the environment, the accumulated wearing, usage, etc. However, due to the lack of accuracy or fluctuation of data, it is oftentimes impossible to evaluate precisely the correct classification rate of faults. In order to classify each type of fault, neural networks and fuzzy logic are two different intelligent diagnosis methods that are more applied now, and each has its own advantages and disadvantages. A new hybrid fault diagnosis approach is introduced in this paper that considers the combined learning algorithm and knowledge base (Fuzzy rules) to handle ambiguous and even erroneous information. Therefore, to enhance the classification accuracy, three perceptron models including: linear perceptron (LP), multilayer perceptron (MLP) and fuzzy perceptron (FP) have been respectively established and compared. The conditional risk function “PDF” that measures the expectation of loss when taking an action is presented at the same time. We evaluate the proposed hybrid approach “Variable Learning Rate Gradient Descent with Bayes’ Maximum Likelihood formula” VLRGD-BML on dataset of milk pasteurization process and compare our approach with other similar published works for fault diagnosis in the literature. Comparative results indicate the higher efficiency and effectiveness of the proposed approach with fuzzy perceptron for uncertain fault diagnosis problem.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0924-669X
    EISSN: 1573-7497
    DOI: 10.1007/s10489-017-1132-8
  • Source: ProQuest One Psychology
    ProQuest Central
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