An integrated fuzzy inference system and AHP approach for criticality analysis of assets: A case study of a gas refinery

2021 ◽  
pp. 1-19
Author(s):  
Majid Mardani Shahri ◽  
Abdolhamid Eshraghniaye Jahromi ◽  
Mahmoud Houshmand
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Integrated Approach ◽  
Human Mind ◽  
Maximum Efficiency ◽  
Risk Matrix ◽  
Inference System ◽  
Criticality Analysis ◽  
Hierarchy Process ◽  
Gas Refinery

The purpose of maintenance is to ensure the maximum efficiency and availability of production assets at optimal cost considering quality, safety, and environmental aspects. Assets criticality analysis is one of the main steps in many maintenance methodologies, including Reliability Centered Maintenance. The present study seeks to provide a solution for determining critical assets for more efficient maintenance management. In this regard, an integrated approach of the analytical hierarchy process and fuzzy inference system was proposed based on the concept of the risk matrix. According to the concept of the risk matrix, two main criteria of failure consequences and probability were employed to determine assets criticality. Analytic Hierarchy Process (AHP) was used to consider all sub-criteria of failure consequences and probability. Finally, using two main criteria as inputs, a fuzzy inference system was developed to determine the criticality of the assets. The proposed approach was implemented in a gas refinery; the results showed its effectiveness and applicability in the process of prioritizing assets based on criticality criteria. The proposed approach has the advantages of multi-criteria decision-making techniques, modeling ambiguity and uncertainty in real issues, modeling the process of inference in the human mind, and storing the knowledge of the organization’s expert.

Failure mode, effects and criticality analysis improvement by using new criticality assessment and prioritization based approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ammar Chakhrit ◽  
Mohammed Chennoufi
Keyword(s):  
Failure Mode ◽  
Conventional Method ◽  
Fuzzy Inference System ◽  
Risk Evaluation ◽  
Failure Modes ◽  
Fuzzy Inference ◽  
Decision Makers ◽  
Inference System ◽  
Content Type ◽  
Criticality Analysis

Purpose This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios. Design/methodology/approach To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method. Findings This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method. Originality/value This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

On the Necessity for Minimizing Risk Based Technology Qualification Variability: An Application to Offshore Floating Wind Turbines

2020 ◽  
Author(s):  
S. M. Samindi M. K. Samarakoon ◽  
R. M. Chandima Ratnayake
Keyword(s):  
Wind Turbines ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
New Technology ◽  
Risk Matrix ◽  
Inference System ◽  
Mode Identification ◽  
Best Available Technology ◽  
Available Technology ◽  
Best Available Technology Bat

Abstract Technology qualification (TQ) has been employed to perform assessments to verify whether a new technology performs within pre-specified functional limits after an application. If a best available technology (BAT) is used in a new environment, it is considered as a new technology. The TQ is vital in the implementation of best available technology (BAT) in a new environment. Risk based technology qualification provides an optimal approach for performing TQ of a BAT when it is necessary to implement in a new environment. This manuscript first demonstrates the standard TQ process. Secondly, it presents development of a risk matrix for failure mode identification and consequence risk ranking (FMI&CRR). Thirdly, it demonstrates the use of FMI&CRR in a risk-based technology qualification process. Finally, it presents use of the risk matrix to perform TQ on moorings solutions that have been selected as a BAT for a floating wind turbine sub-system. Fuzzy inference system has been used to assess the risk rank to minimize the variability that causes due to experts’ performance variability. Illustrative risk based TQ assessment has been performed and presented. The developed risk based TQ process (TQP), fuzzy inference system supported risk rank estimation, and illustrative risk based TQ recommendation are significantly important for practitioners while performing FMI&CRR in larger scale offshore floating wind turbines’ TQ projects.

scholarly journals Modeling and control of an unstable system using probabilistic fuzzy inference system

2015 ◽  
Vol 25 (3) ◽  
pp. 377-396
Author(s):  
N. Sozhamadevi ◽  
S. Sathiyamoorthy
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Integrated Approach ◽  
Fuzzy Rules ◽  
Unstable System ◽  
Inference System ◽  
Modeling And Control ◽  
Highly Nonlinear ◽  
And Control

Abstract A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

scholarly journals Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

2018 ◽  
Vol 10 (12) ◽  
pp. 4780 ◽  
Author(s):  
Min-Sung Kim ◽  
Eul-Bum Lee ◽  
In-Hye Jung ◽  
Douglas Alleman
Keyword(s):  
Risk Assessment ◽  
Analytic Hierarchy Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Raw Materials ◽  
Decision Makers ◽  
Analytic Hierarchy ◽  
Project Risk ◽  
Inference System ◽  
Hierarchy Process

This paper presents an analytic hierarchy process (AHP)-fuzzy inference system (FIS) model to aid decision-makers in the risk assessment and mitigation of overseas steel-plant projects. Through a thorough literature review, the authors identified 57 risks associated with international steel construction, operation, and transference of new technologies. Pairwise comparisons of all 57 risks by 14 subject-matter experts resulted in a relative weighting. Furthermore, to mitigate human subjectivity, vagueness, and uncertainty, a fuzzy analysis based on the findings of two case studies was performed. From these combined analyses, weighted individual risk soring resulted in the following top five most impactful international steel project risks: procurement of raw materials; design errors and omissions; conditions of raw materials; technology spill prevention plan; investment cost and poor plant availability and performance. Risk mitigation measures are also presented, and risk scores are re-assessed through the AHP-FIS analysis model depicting an overall project risk score reduction. The model presented is a useful tool for industry performing steel project risk assessments. It also provides decision-makers with a better understanding of the criticality of risks that are likely to occur on international steel projects.

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