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

Djelloul, Imene; Sari, Zaki; Latreche, Khaled

doi:10.1007/s10489-017-1132-8

Cited by 9 publications

(11 citation statements)

References 31 publications

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“…Recently, Djelloul et al 14 proposed a new hybrid algorithm for fault isolation based on fuzzy Levenberg-Marquardt (FLM) algorithm and genetic algorithm (GA). Another notable work was conducted by Djelloul et al 5 The authors used a classical learning algorithm for the same task and showed that the uncertainty has a considerable impact on fault classification according to other studies reported in the literature. However, how to quickly detect and diagnose faults in production systems with regression tasks has not been investigated.…”

Section: Related Workmentioning

confidence: 97%

“…We propose a generalization of the work in Djelloul et al 5 by introducing (1) convergence speed of the learning algorithm with minimum mean square error (MSE) and (2) the regression task (function approximation) for classification accuracy;…”

Section: Related Workmentioning

confidence: 99%

“…1. We propose a generalization of the work in Djelloul et al 5 by introducing (1) convergence speed of the learning algorithm with minimum mean square error (MSE) and (2) the regression task (function approximation) for classification accuracy; 2. We implement our learning algorithm within the regression task, where each fault should be detected and diagnosed as quickly as possible with minimum ambiguity; 3.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, the following two objectives are considered: the first objective is to choose the best learning algorithm in order to take into account the quick detection of possible faults with minimum error; the second objective is that of how learning algorithm can adversely affect a fault classification process. Using the dataset in Djelloul et al, 5 experimental results are then presented and discussed. The experimental results with the same and new datasets are compared with those of other algorithms published in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems

Djelloul

Sari

Sidibe³

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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Fault diagnosis is becoming an important issue in industrial environment, and the accurate diagnosis is the most significant part in fault handling. This article discusses a fault detection and diagnosis problem for manufacturing systems taking into account rapid detection and speed performances of fault isolation with minimum ambiguity. However, in many complex real plants, it may not be possible to discover accurately the causes of probable faults. The accuracy of fault or fault detection by the traditional approaches is not adequate. Considering the quality effect of the learning algorithm, a new hybrid neural network approach is developed using the integration of a regression task for classification accuracy. Two models of neural networks: gradient descent and momentum & adaptive learning rate and Levenberg–Marquardt are investigated and compared. The performance of the proposed approach is evaluated using mean square error, convergence speed, and classification accuracy. The case study and experimental results are presented and discussed. A comparison with the Levenberg–Marquardt regression approach shows the importance of considering the proposed learning algorithm quality in the fault detection and diagnosis problem compared with those reported in the literature.

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Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems

Djelloul

Sari

Sidibe³

2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…AFNN combines the self-learning ability of neural network with fuzzy system, and uses fuzzy structure of neural network to represent fuzzy processing, fuzzy reasoning and precise calculation of fuzzy systems, thereby, realizing a hybrid neural network of self-organization and self-learning of fuzzy system. Because of its strong adaptability, robustness and fault tolerance, AFNN has been widely used in the field of fault prediction [8], [28]- [30]. Its structure is shown in Fig 2. 1…”

Section: Construction Of Fault Prediction Model a Afnnmentioning

confidence: 99%

A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function

Zhang

et al. 2020

IEEE Access

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As an essential and challenging technology of fault prediction and health management(PHM), fault prediction technology has been a research focus in the field of fault diagnosis. However, the current model-based fault prediction technology and data-driven fault prediction technology have some limitations, and it is difficult to effectively apply them in practice. Therefore, this paper combines the advantages of two kinds of fault prediction technology, sets the fault distribution function as the membership function of the adaptive fuzzy neural network based on the full analysis of the fault mechanism. The use of the fault distribution function to highly generalize the law of fault occurrence, and the strong self-learning ability of the neural network can effectively tap the potential fault information of the fault data, thereby using the fault distribution function to fit the fault data, and forming a set of membership functions by presetting a variety of membership functions, so as to expand the applicability of the proposed model in fault prediction. The experimental results show that the fault prediction model proposed in this paper has the advantages of high prediction accuracy, fast convergence speed and good applicability. INDEX TERMS Adaptive fuzzy neural network, fault mechanism, fault prediction, membership function.

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Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models

Soldevila¹,

Boracchi

Roveri

et al. 2021

Neural Comput & Applic

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Leaks represent one of the most relevant faults in water distribution networks (WDN), resulting in severe losses. Despite the growing research interest in critical infrastructure monitoring, most of the solutions present in the literature cannot completely address the specific challenges characterizing WDNs, such as the low spatial resolution of measurements (flow and/or pressure recordings) and the scarcity of annotated data. We present a novel integrated solution that addresses these challenges and successfully detects and localizes leaks in WDNs. In particular, we detect leaks by a sequential monitoring algorithm that analyzes the inlet flow, and then we validate each detection by an ad hoc statistical test. We address leak localization as a classification problem, which we can simplify by a customized clustering scheme that gathers locations of the WDN where, due to the low number of sensors, it is not possible to accurately locate leaks. A relevant advantage of the proposed solution is that it exposes interpretable tuning parameters and can integrate knowledge from domain experts to cope with scarcity of annotated data. Experiments, performed on a real dataset of the Barcelona WDN with both real and simulated leaks, show that the proposed solution can improve the leak detection and localization performance with respect to methods proposed in the literature.

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

Cited by 9 publications

References 31 publications

Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems

Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems

A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function

Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models

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