A New Hybrid Fault Diagnostic Method for Combining Dependency Matrix Diagnosis and Fuzzy Diagnosis Based on an Enhanced Inference Operator

Shi, Junyou; Chen, Long; Cui, Weiwei

doi:10.1007/s00034-015-0047-z

Cited by 15 publications

(2 citation statements)

References 28 publications

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“…For instance, Zhang et al [19] proposed a hybrid rough sets and support vector machine method to deal with the issue of the fault diagnosis for the hydroelectric generator unit. To address the problem of the fault diagnosis of electronic systems, Shi et al [20] developed a hybrid dependency matrix diagnosis and fuzzy diagnosis method. Zhou et al [21] proposed a hybrid method based on ontology and signal analysis to solve the problem of the fault diagnosis of mechanical components.…”

Section: Problem Statementsmentioning

confidence: 99%

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

Chen

Guo

et al. 2019

Symmetry

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Focusing on the problems of quality information management and quality defects diagnosis in the manufacturing process of large equipment, a novel quality defects diagnosis method based on product gene theory and knowledge base was developed. First, a product gene model and a sectional encoding method for the quality control of the manufacturing process of large equipment were proposed. In that model, the processing surface was the minimum information granularity to meet the production characteristics of large equipment and to improve the flexibility of the product gene model. Then, a similarity evaluation rule and an optimization method of the weights of elements based on particle swarm optimization (PSO) were addressed to filter the available knowledge of product gene from the product gene knowledge base. Aiming at the characteristic of many-to-many between quality defects and quality influence factors in some cases, a fuzzy comprehensive evaluation (FCE) method was developed for the further localization of diagnosis knowledge. Finally, an experiment of bearing spacer was applied to illustrate the proposed quality diagnosis approach. In the experiment, the data from the target gene and knowledge genes were described reasonably. On this basis, available knowledge genes could be accurately filtered with the proposed similarity rule and the method of filtration, where the PSO was proved to be effective. The diagnosis results of the experiment show that multiple factors lead to the defects that were verified. Therefore, the proposed quality defects diagnosis method is an effective way for quality control.

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Section: Problem Statementsmentioning

confidence: 99%

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

Chen

Guo

et al. 2019

Symmetry

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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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ECG Beat Classification Based on Deep Bidirectional Long Short-Term Memory Recurrent Neural Network

Shen

Zhang

et al. 2019

Proceedings of the 2nd International Conference on Healthcare Science and Engineering

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A New Hybrid Fault Diagnostic Method for Combining Dependency Matrix Diagnosis and Fuzzy Diagnosis Based on an Enhanced Inference Operator

Cited by 15 publications

References 28 publications

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

A Novel Quality Defects Diagnosis Method for the Manufacturing Process of Large Equipment Based on Product Gene Theory

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

ECG Beat Classification Based on Deep Bidirectional Long Short-Term Memory Recurrent Neural Network

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