Using a Hybrid Cost-FMEA Analysis for Wind Turbine Reliability Analysis

Tazi, Nacef; Châtelet, Éric; Bouzidi, Youcef

doi:10.3390/en10030276

Cited by 76 publications

(53 citation statements)

References 34 publications

(50 reference statements)

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“…The average matrix D was calculated based on pairwise comparisons according to five-levelled scales (0 = no effect, 1 = low effect, 2 = media effect, 3 = high effect, 4 = very high effect). Then, the total influence matrix T was derived from Equations (8)- (10). In sum, the total relation matrix for the main clusters is shown in Table 5.…”

Section: Interrelationships Among Clusters and Fmsmentioning

confidence: 99%

“…Also, the comparisons of the proposed framework with previous studies demonstrated the multidisciplinary applications of the present framework. Energies 2019, 12, 649 2 of 22Studies have shown that FMEA has a wide area of application that ranges from wind turbine reliability, healthcare, automotive, nuclear, and manufacturing [9][10][11][12][13]. Moreover, in the electrical distribution field, FMEA has been used in the determination of critical equipment and is mostly combined with a reliability centered maintenance (RCM) to improve the maintenance reliability [14].The work of Matteson [15] shows the identification process for FMs and their consequences with the classification of the severity and occurrence.…”

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confidence: 99%

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Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework

et al. 2019

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The oil and gas industry is looking for ways to accurately identify and prioritize the failure modes (FMs) of the equipment. Failure mode and effect analysis (FMEA) is the most important tool used in the maintenance approach for the prevention of malfunctioning of the equipment. Current developments in the FMEA technique are mainly focused on addressing the drawbacks of the conventional risk priority number calculations, but the group effects and interrelationships of FMs on other measurements are neglected. In the present study, a hybrid distribution risk assessment framework was proposed to fill these gaps based on the combination of modified linguistic FMEA (LFMEA), Analytic Network Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) techniques. The hybrid framework of FMEA was conducted in a hazardous environment at a power generation unit in an oil and gas plant located in Yemen. The results show that mechanical and gas leakage FM in electrical generators posed a greater risk, which critically affects other FMs within the plant. It was observed that the suggested framework produced a precise ranking of FMs, with a clear relationship among FMs. Also, the comparisons of the proposed framework with previous studies demonstrated the multidisciplinary applications of the present framework.

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Section: Interrelationships Among Clusters and Fmsmentioning

confidence: 99%

mentioning

confidence: 99%

Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework

et al. 2019

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“…In the Definition 6, we replace p i and p j with p w i i and p w i j , respectively, according to Equation (6). Similar to the proof of Theorem 1, we obtained IVPFWGBM x,y (p 1 , p 2 , · · · , pn) = 1…”

Section: Appendixmentioning

confidence: 85%

“…As a formal system analysis approach, the technique of FMEA was originally proposed in the 1960s, and was intended to satisfy the aerospace industry's apparent safety and reliability requirements [4]. Since then, the FMEA method has been successfully used in various industries as a simple and powerful tool to enhance the safety and reliability of products, systems, and processes in industry, such as the aerospace [5], energy [6], heath care and hospital [7], and manufacturing industries [8].…”

Section: Introductionmentioning

confidence: 99%

Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence

Zhu

Wang

2018

Algorithms

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Failure mode and effects analysis is an effective and powerful risk evaluation technique in the field of risk management, and it has been extensively used in various industries for identifying and decreasing known and potential failure modes in systems, processes, products, and services. Traditionally, a risk priority number is applied to capture the ranking order of failure modes in failure mode and effects analysis. However, this method has several drawbacks and deficiencies, which need to be improved for enhancing its application capability. For instance, this method ignores the consensus-reaching process and the correlations among the experts' preferences. Therefore, the aim of this study was to present a new risk priority method to determine the risk priority of failure modes under an interval-valued Pythagorean fuzzy environment, which combines the extended Geometric Bonferroni mean operator, a consensus-reaching process, and an improved Multi-Attributive Border Approximation area Comparison approach. Finally, a case study concerning product development is described to demonstrate the feasibility and effectiveness of the proposed method. The results show that the risk priority of failure modes obtained by the proposed method is more reasonable in practical application compared with other failure mode and effects analysis methods.Algorithms 2018, 11, 34 2 of 24 of limitations. Numerous alternative methods have been presented in the literature to handle some of these drawbacks [2], which could explain why focusing on these shortcomings can improve the performance of traditional FMEA. However, many practical application cases have suggested that the ranking results determined by these alternative methods are not reliable and suffer from drawbacks in some situations. The main reasons are the assumption that the experts' preferences are independent and the team of experts have reached a consensus before aggregating the experts' preferences. Therefore, it is worth studying the problem of how to determine effectively the risk priority of failure modes without considering these two assumptions.It is very important for an FMEA team to adopt a suitable aggregation method to aggregate the experts' preferences into a collective evaluation matrix before determining the failure modes ranking. Many aggregation methods that assume that experts' preferences are independent have been applied to aggregate the experts' preferences. In practice, the FMEA team experts come from different departments or industries, and their subjective preferences, which are often influenced by social, power, knowledge, and other factors, usually indicate some interdependent characteristics. On the one hand, the geometric Bonferroni mean (GBM) initially proposed by Xia et al. [9] is an efficient operator to deal with the aggregation of interdependent arguments. The GBM operator has a prominent characteristic that can easily capture the interrelationships among input arguments [9]. Therefore, it is significant to integrate the experts' preferences...

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“…Wind farm reliability is a subject of interest to industry and academia [1][2][3][4]. The availability of wind as a resource alongside the reliability of wind turbine generators may significantly affect power system reliability [5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Impact Evaluation of Wind Power Geographic Dispersion on Future Operating Reserve Needs

et al. 2018

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This paper evaluates the potential of diverse wind power patterns to balance the global power output of wind farms using the concept of operating reserve assessment. To achieve this, operating reserve assessment models are utilized to evaluate bulk generation systems under several conditions of wind power geographic distribution. Different wind behavior patterns and wind power penetration levels are tested using a modified configuration of the Institute of Electrical and Electronics Engineers Reliability Test System 96 (IEEE RTS-96). The results highlight that on a large country scale system with different wind characteristics, the diversification of wind behavior might be conducive to a compensation of wind power fluctuations, which may significantly decrease the need for system operating reserves. This effect is verified using probability distribution functions of reserve needs estimated by sequential Monte Carlo simulations (SMCS), such that useful information regarding generation capacity flexibility is drawn from the evaluations.

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Using a Hybrid Cost-FMEA Analysis for Wind Turbine Reliability Analysis

Cited by 76 publications

References 34 publications

Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework

Identification and Prioritization of Risk Factors in an Electrical Generator Based on the Hybrid FMEA Framework

Failure Mode and Effects Analysis Considering Consensus and Preferences Interdependence

Impact Evaluation of Wind Power Geographic Dispersion on Future Operating Reserve Needs

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