Fault diagnosis using Rough Sets Theory

Shen, Lixiang; Tay, Francis Eng Hock; Qu, Liangsheng; Shen, Yuhao

doi:10.1016/s0166-3615(00)00050-6

Cited by 157 publications

(58 citation statements)

References 6 publications

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“…Lee and White [50] presented an enhancement scheme to aid the measurement and characterization of impulsive sounds and vibration signals for fault detection in reciprocating machineries. Shen et al [51] developed rough sets theory to diagnose the valve fault for a multi-cylinder diesel engine, while considering the complex structure of the engine and the presence of multi-excite sources. Wang and Hu [52] investigated the use of basic fuzzy logic principle as a fault diagnostic technique for five-plunge pump used in oil field.…”

Section: Reciprocating Machineriesmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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Section: Reciprocating Machineriesmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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“…The most popular learning systems capable of extracting logic rules from examples are probably the Classification Trees (CTs). However, the experiences of some authors' show that the classification systems obtained from the Rough Sets Theory (RST) exhibit significant advantages over CTs [5][6][7][8][9] and seem to be their newer alternative in many process industry applications.…”

Section: Generation Of Design Rulesmentioning

confidence: 99%

Optimization of Side Feeders Systems by Means of Simulation of Solidification

Perzyk¹,

Kozłowski²,

Mazur³

et al. 2015

Archives of Foundry Engineering

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Simulation software can be used not only for checking the correctness of a particular design but also for finding rules which could be used in majority of future designs. In the present work the recommendations for optimal distance between a side feeder and a casting wall were formulated. The shrinkage problems with application of side feeders may arise from overheating of the moulding sand layer between casting wall and the feeder in case the neck is too short as well as formation of a hot spot at the junction of the neck and the casting. A large number of simulations using commercial software were carried out, in which the main independent variables were: the feeder's neck length, type and geometry of the feeder, as well as geometry and material of the casting. It was found that the shrinkage defects do not appear for tubular castings, whereas for flat walled castings the neck length and the feeders' geometry are important parameters to be set properly in order to avoid the shrinkage defects. The rules for optimal lengths were found using the Rough Sets Theory approach, separately for traditional and exothermic feeders.

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“…The knowledge extracted was in the form of "actual control applied  performance obtained" and the knowledge generated could be used to increase the accuracy of the system or validate the performance model. A rough set theory based approach was used by Shen et al [34] to get the final reducts and extract the rules for fault diagnosis of diesel engines. These rules were used to distinguish the fault type or to inspect the dynamic characteristics of the machinery.…”

Section: Concept Description (Characterization and Discrimination) Inmentioning

confidence: 99%

Data mining in manufacturing: a review based on the kind of knowledge

2008

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In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

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Fault diagnosis using Rough Sets Theory

Cited by 157 publications

References 6 publications

Basic research on machinery fault diagnostics: Past, present, and future trends

Basic research on machinery fault diagnostics: Past, present, and future trends

Optimization of Side Feeders Systems by Means of Simulation of Solidification

Data mining in manufacturing: a review based on the kind of knowledge

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