An analytical model of electronic fault diagnosis on extension of the dependency theory

Cui, Yiqian; Shi, Junyou; Wang, Zili

doi:10.1016/j.ress.2014.09.015

Cited by 30 publications

(12 citation statements)

References 26 publications

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“…The analytical model-based approach [2][3][4] has great limitations in application due to the need to establish accurate mathematical models. Knowledge-based methods are mainly divided into two types: causal graph method [5][6][7] and fault tree method [8].…”

Section: Related Workmentioning

confidence: 99%

Multi-source fault detection and diagnosis based on multi-level Knowledge Graph and Bayesian theory reasoning (S)

Sun¹,

Wang²

2019

International Conferences on Software Engineering and Knowledge Engineering

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In complex industrial processes, complex associations are often involved. This complex relationship makes traditional fault detection and diagnosis methods difficult to achieve satisfactory results in multi-source fault detection and diagnosis. Therefore, this paper proposes a new multi-source fault detection and diagnosis framework. The method can successfully detect the state of the system, and at the same time, can locate the fault source simply and quickly. This method firstly constructs the multi-level knowledge graph in the complex industrial process, and then use the discriminant coefficient R to detect whether the system has failed. If the system fails into the fault diagnosis stage, the probability of the fault is derived based on Bayesian theory. This article describes the framework in detail. The TE process is taken as an example to prove the effectiveness of the method. Keywords-complex industrial process, knowledge graph, bayesian theory, fault detection and diagnosisI.

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Section: Related Workmentioning

confidence: 99%

Multi-source fault detection and diagnosis based on multi-level Knowledge Graph and Bayesian theory reasoning (S)

Sun¹,

Wang²

2019

International Conferences on Software Engineering and Knowledge Engineering

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show abstract

“…The current fault diagnosis methods are numerous and have been built on one another effectively. In general, fault diagnosis methods are mainly divided into the following parts: methods based on analytical models; methods based on qualitative knowledge; and methods based on data . Initially, fault diagnosis research is mostly based on an analytical model and, consequently, many achievements in the field include state estimation methods, parameter estimation methods, and equivalent space methods .…”

Section: Related Workmentioning

confidence: 99%

A fault detection method based on horizontal visibility graph‐integrated complex networks: Application to complex chemical processes

Geng

Wang

et al. 2018

Can J Chem Eng

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With the trend towards large-scale structure and complexity in modern chemical processes, it is difficult to describe the system operating status and fault conditions via traditional approaches. In addition, there are obstacles to fault detection research in complex chemical processes due to the characteristics of the equipment hardware used in chemical systems, data collection and handling, strong internal correlations in chemical systems, factors affecting transmission, and randomness and cascade in process failures. Therefore, this paper presents a fault detection method based on horizontal visibility graph (HVG) analysis-integrated complex networks. The data for each variable in the system are regarded as a time series, each time series is modelled into a network by the horizontal visibility algorithm, and each single-layer network corresponding to a time series is abstracted as a node. Meanwhile, the correlation between two single-layer networks is used to characterize the correlation between the corresponding nodes. Moreover, a complex network structure representing the chemical system can be constructed from the correlations. In addition, according to the correlation ratio matrix obtained from the fault state and the normal state, the variance of each node determines the faulty node. Finally, we verify the validity and the effectiveness of the proposed method by applying it to the Tennessee Eastman (TE) process.

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“…. , }, and the dependency matrix is a Boolean matrix with × dimensions, denoted as follows [59,60]: A test set that can be affected by the fault mode is defined as ( ) = { | = 1, ∀ }. Similarly, a fault set that can be detected by the test is defined as ( ) = { | = 1, ∀ }.…”

Section: Fault Test Dependency Matrixmentioning

confidence: 99%

An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO

Zhou

Tang

et al. 2018

Complexity

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Sensor data-based test selection optimization is the basis for designing a test work, which ensures that the system is tested under the constraint of the conventional indexes such as fault detection rate (FDR) and fault isolation rate (FIR). From the perspective of equipment maintenance support, the ambiguity isolation has a significant effect on the result of test selection. In this paper, an improved test selection optimization model is proposed by considering the ambiguity degree of fault isolation. In the new model, the fault test dependency matrix is adopted to model the correlation between the system fault and the test group. The objective function of the proposed model is minimizing the test cost with the constraint of FDR and FIR. The improved chaotic discrete particle swarm optimization (PSO) algorithm is adopted to solve the improved test selection optimization model. The new test selection optimization model is more consistent with real complicated engineering systems. The experimental result verifies the effectiveness of the proposed method.

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An analytical model of electronic fault diagnosis on extension of the dependency theory

Cited by 30 publications

References 26 publications

Multi-source fault detection and diagnosis based on multi-level Knowledge Graph and Bayesian theory reasoning (S)

Multi-source fault detection and diagnosis based on multi-level Knowledge Graph and Bayesian theory reasoning (S)

A fault detection method based on horizontal visibility graph‐integrated complex networks: Application to complex chemical processes

An Improved Test Selection Optimization Model Based on Fault Ambiguity Group Isolation and Chaotic Discrete PSO

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