A novel failure mode effect and criticality analysis (FMECA) using fuzzy rule-based method: A case study of industrial centrifugal pump

Gupta, Gourav; Ghasemian, Hamed; Janvekar, Ayub Ahmed

doi:10.1016/j.engfailanal.2021.105305

Cited by 19 publications

(7 citation statements)

References 35 publications

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“…This study used the fuzzy computational process to improve RPN estimation, resulting in a more accurate criticality ranking determination. Although many researchers have reported the integration of fuzzy computational processes in FMEA/FMECA to improve RPN estimation (Kumru and Kumru, 2013;Da gsuyu et al, 2016;Felecia, 2014;Gupta and Mishra, 2017;George et al, 2019;Ahmed and Gu, 2020;Buffa et al, 2020;Gupta et al, 2021), their studies were conducted and discussed in a specific scope of FMEA/FMECA application. In other words, the continuation processes of fuzzy RPN estimation to criticality ranking determination and Awad and As'ad (2016) presented a comprehensive RCM methodology that fundamentally used the same direction of idea as the RCM methodology proposed in this paper.…”

Section: Journal Of Quality In Maintenance Engineeringmentioning

confidence: 99%

“…, 2019; Ahmed and Gu, 2020; Buffa et al. , 2020; Gupta et al. , 2021), their studies were conducted and discussed in a specific scope of FMEA/FMECA application.…”

Section: Rcm Applications – An Overviewmentioning

confidence: 99%

“…Another typical version of RPN calculation is based on severity (S), occurrence (O) , and detection (D) parameters. The failure severity describes the effect of a particular failure mode, whereas occurrence describes the failure mode frequency, and detection describes the likelihood of failure mode detection (Gupta et al ., 2021). The RPN is determined based on the multiplication of S, O , and D , as shown in Equation (3).…”

Section: The Proposed Rcm Framework With Fuzzy Computational Processmentioning

confidence: 99%

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A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

Ali Ahmed Qaid,

Ahmad,

Mustafa

et al. 2024

JQME

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PurposeThis study presents a systematic framework for maintenance strategy development of manufacturing process machinery. The framework is developed based on the reliability-centred maintenance (RCM) approach to minimise the high downtime of a production line, thus increasing its reliability and availability. A case study of a production line from the ghee and soap manufacturing industry in Taiz, Yemen, is presented for framework validation purposes. The framework provides a systematic process to identify the critical system(s) and guide further investigation for functional significant items (FSIs) based on quantitative and qualitative analyses before recommending appropriate maintenance strategies and specific tasks.Design/methodology/approachThe proposed framework integrates conventional RCM procedure with the fuzzy computational process to improve FSIs criticality estimation, which is the main part of failure mode effect criticality analysis (FMECA) applications. The framework consists of four main implementation stages: identification of the critical system(s), technical analysis, Fuzzy-FMECA application for FSIs criticality estimation and maintenance strategy selection. Each stage has its objective(s) and related scientific techniques that are applied to systematically guide the framework implementation.FindingsThe proposed framework validation is summarised as follows. The first stage results demonstrate that the seaming system (top and bottom systems) caused 50% of the total production line downtime, indicating it is a critical system that requires further analysis. The outcomes of the second stage provide significant technical information on the subject (seaming system), helping team members to identify and understand the structure and functional complexities of the seaming system. This stage also provides a better understanding of how the seaming system functions and how it can fail. In stage 3, the application of FMECA with the fuzzy computation integration process presents a systematic way to analyse the failure mode, effect and cause of items (components of the seaming system). This stage also includes items’ criticality estimation and ranking assessment. Finally, stage four guides team members in recommending the appropriate countermeasures (maintenance strategies and task selection) based on their priority level.Originality/valueThis paper proposes an original maintenance strategies development framework based on the RCM approach for production system equipment. Specifically, it considers a fuzzy computational process based on the Gaussian function in the third stage of the proposed framework. Adopting the fuzzy computational process improves the risk priority number (RPN) estimation, resulting in better criticality ranking determination. Another significant contribution is introducing an extended item criticality ranking assessment process to provide maximum levels of criticality item ranking. Finally, the proposed RCM framework also provides detailed guidance on maintenance strategy selection based on criticality levels, unique functionality and failure characteristics of each FSI.

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Section: Journal Of Quality In Maintenance Engineeringmentioning

confidence: 99%

“…, 2019; Ahmed and Gu, 2020; Buffa et al. , 2020; Gupta et al. , 2021), their studies were conducted and discussed in a specific scope of FMEA/FMECA application.…”

Section: Rcm Applications – An Overviewmentioning

confidence: 99%

Section: The Proposed Rcm Framework With Fuzzy Computational Processmentioning

confidence: 99%

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A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

Ali Ahmed Qaid,

Ahmad,

Mustafa

et al. 2024

JQME

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“…The researchers in [18] proposed a fuzzy-rule-based model incorporating the Gupta-Ghasemian formula and the Dempster-Shafer theory to quantify uncertainty. Their study analyzed 20 failure modes of an industrial centrifugal pump using an FMEA.…”

Section: Literature Reviewmentioning

confidence: 99%

Failure Mode and Effect Analysis with a Fuzzy Logic Approach

Cardiel-Ortega,

Baeza-Serrato

2023

Systems

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Failure mode and effect analysis (FMEA) is one of the most used techniques in risk management due to its potential to solve multidisciplinary engineering problems. The role of experts is fundamental when developing the FMEA; they identify the failure modes by expressing their opinion based on their experience. A relevant aspect is a way in which the experts evaluate to obtain the indicator of the risk priority number (RPN), which is based on qualitative analysis and a table of criteria where they subjectively and intuitively determine the factor level (severity, occurrence, and detection) for each of the failures. With this, imprecision is present due to the interpretation that each one has regarding the failures. Therefore, this research proposes a fuzzy logic evaluation system with a solid mathematical basis that integrates these conditions of imprecision and uncertainty, thus offering a robust system capable of emulating the evaluation form of experts to support and improve decision making. One of the main contributions of this research is in the defuzzification stage, adjusting the centroid method and treating each set individually. With this, the RPN values approximate to the conventional technique were obtained. Simulations were carried out to test and determine the system’s best structure. The system was validated in a textile company in southern Guanajuato. The results demonstrate that the system reliably represents how experts perform risk assessment.

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“…Methods for increasingly frequent monitoring and measurement of the program 'system' are experimented with, including the use of Bayesian statistical approaches. Attempts could be made to apply external quality improvement through unified "mash-up" reviews with other panels currently reviewing the same Novel applications of expert opinion, the Dempster-Shafer theory, or fuzzy rules could be attempted [39]. Because of the pace of technology improvement and new inventions, not all innovations can be adequately captured and described here.…”

Section: Ml5 Innovatingmentioning

confidence: 99%

<strong>A Maturity Model for the Scientific Review of Clinical Trial Designs and Their Informativeness </strong>

Dolley¹,

Norman²,

McNair³

et al. 2023

Preprint

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Many clinical trials end uninformatively. Informativeness, in the context of clinical trials, defines whether a study’s results definitively answer its research questions with meaningful next steps. One subset of these trials are those focused on global health set in low-resource settings. Global health clinical trials benefitting people in low-resource settings are funded primarily by a limited number of large foundations, pharmaceutical firms (“industry”), and national governments. While clinical trial protocols are required to go through reviews in regulatory and ethical domains, outside of industry-funded trials, funders rarely require focused scientific design reviews. There are no documented standards and processes, or even best practices, for funders to perform scientific reviews after the funding commitment. Considering the investment in and standardization of ethical and regulatory reviews, and the prevalence of studies ending without clarity or never finishing, it may be that scientific reviews of trial designs with a focus on informativeness offer the best chance for improved outcomes and return on investments in clinical trials. A maturity model is a helpful tool for knowledge transfer to help grow capabilities in a new area, or for those looking to perform a self-assessment in an existing area. Such a model is offered for scientific design reviews of clinical trial protocols: a valuable and often-neglected governance step for funders or sponsors, among others. This maturity model includes 11 process areas and 5 maturity levels. Each of the 55 process area levels is populated with descriptions on a continuum toward an optimal state to improve trial protocols in the area of risk of failure. This tool allows for prescriptive guidance on next investments to improve attributes of post-funding reviews of trials, with a focus on informativeness.

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A novel failure mode effect and criticality analysis (FMECA) using fuzzy rule-based method: A case study of industrial centrifugal pump

Cited by 19 publications

References 35 publications

A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

A systematic reliability-centred maintenance framework with fuzzy computational integration – a case study of manufacturing process machinery

Failure Mode and Effect Analysis with a Fuzzy Logic Approach

<strong>A Maturity Model for the Scientific Review of Clinical Trial Designs and Their Informativeness </strong>

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