Fuzzy assessment of FMEA for engine systems

Xu, Kai; Tang, Loon Ching; Xie, Min; Ho, Sze Ling; Zhu, Mei-Lin

doi:10.1016/s0951-8320(01)00101-6

Cited by 323 publications

(177 citation statements)

References 9 publications

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“…They stated based on the proposed approach and methodologies, a prototype system named EPDS-1, which can assist inexperienced users to perform FMEA analysis for quality and reliability improvement, alternative design evaluation, materials selection, and cost assessment, thus helping to enhance robustness of new products at the conceptual design stage. Xu et al (2002) presented a fuzzy-logicbased method for FMEA to evaluate engine systems. Tay and Lim (2006) proposed a generic method to simplify the fuzzy logic-based failure mode by reducing the number of rules required by FMEA users for the fuzzy risk priority number (RPN) modeling process.…”

Section: Fuzzy Fmeamentioning

confidence: 99%

Underground mine risk assessment by using FMEA in the presence of uncertainty

Shariati¹

2014

10.5267/j.dsl

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Managers always look for systems with minimum hazards, which cause problems for performance of projects. The largest and the most important hazards of working underground mines can be associated with health, safety and environmental Failure mode and effects analysis (FMEA) is a widely used technique to identify the potential failure modes for measuring reliability of a product or a process. FMEA is performed by developing a risk priority number (RPN), which is the product of severity, occurrence, and detection ratings. On the other hand, with regard to uncertainty in the decision-making, fuzzy theory can help model the inherent uncertainty involved in the underground mining projects. Fuzzy FMEA provides a tool that can work in a better way with vague concepts using insufficient information compared with conventional FMEA. The comparison between the results of the conventional FMEA with those of the proposed model shows that the fuzzy model has a high potential to formulate the level of risk.

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Section: Fuzzy Fmeamentioning

confidence: 99%

Underground mine risk assessment by using FMEA in the presence of uncertainty

Shariati¹

2014

10.5267/j.dsl

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“…The mapping provides a basis from which decisions can be made on the basis of both of qualitative and quantitative information. The process of fuzzy inference involves the developments of qualitative descriptors, MFs, fuzzy logic operations and fuzzy rule base [21][22][23]. The FIS as shown in Fig.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

“…The centroid of area method, which determines the centre of gravity of an aggregated fuzzy set, is the most frequently used method in the fuzzy reasoning systems [3,8,11,13,23], defined as…”

Section: Defuzzificationmentioning

confidence: 99%

An Intelligent Railway Safety Risk Assessment Support System for Railway Operation and Maintenance Analysis

An¹,

Lin²,

Huang³

2013

TOTJ

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Abstract:The paper presents the development of an intelligent railway safety risk assessment based support system. The proposed method can evaluate qualitative and quantitative safety risk data and information in a uniform manner for railway safety risk assessment. It permits the safety risk analysts to assess the risks associated with the failure modes directly using linguistic terms, i.e. qualitative descriptors. The proposed intelligent railway safety risk assessment system is capable of assessing the risks at component level, sub-system level and system level. It can assess not only "hard" risks (e.g. risks of a system), but also "soft" risks (e.g. staff risks). The outcomes of safety risk assessment are represented in two formats, risk score and risk category with a belief of percentage, which provide very useful safety risk information to railway designers, operators, engineers and maintainers for risk response decision making. An illustrative example of staff risk assessment in a railway depot is used to demonstrate the proposed intelligent railway safety risk assessment system. The results indicate that by using the proposed system, risks associated with a railway depot can be assessed effectively and efficiently.

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“…A fuzzy assessment for failure mode effects analysis was presented by Xu and Tang [4] for failure modes with uncertain and imprecise information in engine systems. Brandsaeter [5] described a quantitative risk assessment for human, environmental and property health and safety in the offshore industry.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

et al. 2017

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Abstract:The high hazard mechanical system (HHMS) has three characteristics in the petroleum and petrochemical industry (PPI): high risk, high cost, and high technology requirements. For a HHMS, part, component, and subsystem failures will result in varying degrees and various types of risk consequences, including unexpected downtime, production losses, economic costs, safety accidents, and environmental pollution. Thus, obtaining the quantitative risk level and distribution in a HHMS to control major risk accidents and ensure safe production is of vital importance. However, the structure of the HHMS is more complex than some other systems, making the quantitative risk analysis process more difficult. Additionally, a variety of uncertain risk data hinder the realization of quantitative risk analysis. A few quantitative risk analysis techniques and studies for HHMS exist, especially in the PPI. Therefore, a study on the quantitative risk analysis method for HHMS was completed to obtain the risk level and distribution of high-risk objects. Firstly, Fuzzy Set Theory (FST) was applied to address the uncertain risk data for the occurrence probability (OP) and consequence severity (CS) in the risk analysis process. Secondly, a fuzzy fault tree analysis (FFTA) and a fuzzy event tree analysis (FETA) were used to achieve quantitative risk analysis and calculation. Thirdly, a fuzzy bow-tie model (FBTM) was established to obtain a quantitative risk assessment result according to the analysis results of the FFTA and FETA. Finally, the feasibility and practicability of the method were verified with a case study on the quantitative risk analysis of one reciprocating pump system (RPS). The quantitative risk analysis method for HHMS can provide more accurate and scientific data support for the development of Asset Integrity Management (AIM) systems in the PPI.

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Fuzzy assessment of FMEA for engine systems

Cited by 323 publications

References 9 publications

Underground mine risk assessment by using FMEA in the presence of uncertainty

Underground mine risk assessment by using FMEA in the presence of uncertainty

An Intelligent Railway Safety Risk Assessment Support System for Railway Operation and Maintenance Analysis

A Quantitative Risk Analysis Method for the High Hazard Mechanical System in Petroleum and Petrochemical Industry

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