Fault diagnosis of multi-state gas monitoring network based on fuzzy Bayesian net

Xue, Sisheng; Li, Xiangong; Wang, Xufeng

doi:10.1007/s00779-019-01237-w

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…The empirical knowledge of experts constitutes the belief rule base, and these rules define the inputs and outputs of the BRB model. Therefore, it is not possible to combine FTA and BRB, but some method is needed to embed the FTA of the milling system into the hierarchical BRB model as a way to convert between FTA and BRB [31].…”

Section: Brb Modelling Process Of Milling Fault Detection Methods 311...mentioning

confidence: 99%

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Cheng¹,

Long²,

Wang³

et al. 2023

Computer Systems Science and Engineering

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Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA), the logical correspondence between FTA and BRB is sorted out. This can effectively embed the FTA mechanism into the BRB expert knowledge base. The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion. Secondly, evidence reasoning (ER) is used to ensure the transparency of the model reasoning process. Thirdly, the projection covariance matrix adaptation evolutionary strategies (P-CMA-ES) is used to optimize the model. Finally, this paper verifies the validity model and the method's feasibility techniques for milling data sets.

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Section: Brb Modelling Process Of Milling Fault Detection Methods 311...mentioning

confidence: 99%

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Cheng¹,

Long²,

Wang³

et al. 2023

Computer Systems Science and Engineering

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“…They have been extensively researched and used in a variety of industrial fault diagnosis scenarios [7,8]. For example, Xue et al [9] used fuzzy rules to calculate the fuzzy probability of each mode in a BN fault in a gas-monitoring system. Lin et al [10] dealt with operational uncertainties in evidence and historical information.…”

Section: Overview Of Information Uncertainties In Fault Diagnosis Usi...mentioning

confidence: 99%

“…Conversely, a larger weight was required. The entropy was calculated using (9), and the entropy weight of each expert was obtained using (10).…”

Section: Determining Priority Probability With Expert Knowledge Uncer...mentioning

confidence: 99%

Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems

Liu

Wang

2023

Applied Sciences

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Intelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfer introduces additional uncertainties. However, because industrial equipment has complex fault causes and performances, it is typically difficult or expensive to obtain exact fault probabilities. Therefore, in this study, we proposed an innovative failure detection and diagnosis model for intelligent instruments in an IoT system using a Bayesian network, with a focus on handling uncertainties in expert knowledge and IoT monitoring information. The model addresses the challenge of complex fault causes and performances in industrial equipment, which make the obtainment of exact fault probabilities difficult or expensive. The trapezoidal intuitionistic fuzzy number (TrIFN)-based entropy method was applied in order to aggregate expert knowledge to generate priority probability, and the Leaky-OR gate was used to calculate CPT. The effectiveness of the proposed strategy was demonstrated through its application to an intelligent pressure transmitter (IPT) using the GeNIe software.

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“…The fuzzy Bayesian network and the Fuzzy Dynamic Bayesian Network can solve the kind of problems. The former is a model that combines the fuzzy set theory and the simple Bayesian network, [22][23][24][25][26][27][28][29] which is premised on the basis of the hypothesis that the system reliability state is independent of time. The latter is a model that combines the fuzzy set theory and the dynamic Bayesian network, [30][31][32] which is premised on the basis of the hypothesis that the system reliability state is related to time and changes with time.…”

Section: Approachmentioning

confidence: 99%

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

Zhi-qiang

Zhu

Zhang

et al. 2020

International Journal of Distributed Sensor Networks

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The reliability of face recognition system has the characteristics of fuzziness, randomness, and continuity. In order to measure it in unconstrained scenes, we find out and quantify key broad-sense and narrow-sense influencing factors of reliability on the basis of analyzing operation states for six dynamic face recognition systems in the practical use of six public security bureaus. In this article, we propose a novel evaluation method with True Positive Identification Rate in dynamic and M:N mode and create a novel evaluation model of system reliability with the improved Fuzzy Dynamic Bayesian Network. Subsequently, we infer to solve the fuzzy reliability state probabilities of the six systems with Netica and get two most important factors with the improved fuzzy C-means algorithm. We verify the model by comparing the evaluation results with actual achievements of these systems. Finally, we find several vulnerabilities in the system with the least reliability and put forward a few optimization strategies. The proposed method combines advantages of the improved fuzzy C-means model with those of the dynamic Bayesian network to evaluate the reliability of the dynamic face recognition systems, making the evaluation results more reasonable and realistic. It starts a new research of face recognition systems in unconstrained scenes and contributes to the research on face recognition performance evaluation and system reliability analysis. Besides, the proposed method is of practical significance in improving the reliability of the systems in use.

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Fault diagnosis of multi-state gas monitoring network based on fuzzy Bayesian net

Cited by 5 publications

References 16 publications

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems

Reliability evaluation of dynamic face recognition systems based on improved Fuzzy Dynamic Bayesian Network

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