Enhanced GO methodology to support failure mode, effects and criticality analysis

Liu, Linlin; Fan, Dongming; Wang, Zili; Yang, Dezhen; Cui, Jingjing; Ma, Xinrui

doi:10.1007/s10845-017-1336-0

Cited by 20 publications

(7 citation statements)

References 33 publications

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“…Cost [4,6,7,24], customer demand [33,34], and quality [7] are integrated into FMEA to solve the third shortcoming in many studies. For the fourth shortcoming of traditional FMEA, the Bayesian network (BN) method [16,[35][36][37][38][39] is the most common solution, and other methods include fuzzy cognitive maps [1], FTA analysis [25], and DEMATEL [40][41][42]. To address the fifth shortcoming, scholars have proposed MCDM to evaluate the potential failure based on three criteria to avoid direct calculation of the RPN.…”

Section: Traditional Fmea and Its Improvements At Presentmentioning

confidence: 99%

The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

2021

Complexity

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The risk priority number (RPN) calculation method is one of the critical subjects of failure mode and effects analysis (FMEA) research. Recently, RPN research under a fuzzy uncertainty environment has become a hot topic. Accordingly, increasing studies have ignored the important impact of the random sampling uncertainty in the FMEA assessment. In this study, a fuzzy beta-binomial RPN evaluation method is proposed by integrating fuzzy theory, Bayesian statistical inference, and the beta-binomial distribution. This model can effectively realize real-time, dynamic, and long-term evaluation of RPN under the condition of continuous knowledge accumulation. The major contribution of the proposed model is to use the random uncertainty and fuzzy uncertainty in an integrated model and provide a Markov Chain Monte Carlo (MCMC) method to solve the complex integrated model. The study presented a case study, which presented how to apply this model in practice and indicated the significant influence on the measurement error caused by ignoring the random uncertainty caused by expert evaluation in RPN calculations.

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Section: Traditional Fmea and Its Improvements At Presentmentioning

confidence: 99%

The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

2021

Complexity

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“…Deng et al [23] studied the vulnerability of subway system by network theory and FMECA. Liu et al [24] used GO methodology to enhance FMECA in a qualitative and quantitative way. By analyzing the existing research of FMEA/FMECA, we find that the main purpose of those research is to improve the traditional FMECA by alleviating those shortcomings.…”

Section: B Fault Mode and Effects Analysismentioning

confidence: 99%

A Hybrid Multilevel FTA-FMEA Method for a Flexible Manufacturing Cell Based on Meta-Action and TOPSIS

Zhang

Ran

et al. 2019

IEEE Access

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Fault analysis activity is very important for a flexible manufacturing cell (FMC) in the development phase. Fault tree analysis (FTA) and fault mode and effects analysis (FMEA) are widely used for fault analysis. However, they are time-consuming and expensive when fully implemented. In this paper, we propose an improving hybrid multilevel FTA-FMEA method that is the combination of the above two methods. The proposed method has a clear three-layer analysis structure. In the first layer, a system FTA of the FMC is performed to determine the functional fault modes. Then, FMEA is conducted to examine them and the key functional fault modes are selected by criticality analysis. In the second layer, we perform the FTA of the determined key functional fault modes to find out the meta-action/component fault modes. The bottom events of fault trees in this layer show differences due to the subsystems with different features. Same as the first layer, FMEA is conducted subsequently and criticality analysis is also used to determine the key meta-action/component fault modes. In the last layer, we perform the FTA of the determined key meta-action/component fault modes to find out fault causes. Then, the key fault causes are determined by criticality analysis. Risk priority number (RPN) is usually used to determine the priority ranking in criticality analysis, while its calculation way is slightly naïve. In this paper, we use the technique for order preference by similarity to ideal solution (TOPSIS) method to examine the priority ranking of fault modes/causes. Moreover, we consider the correction cost as the fourth indicator to assess the priority. The improving fault analysis method can not only help designers better understand the new FMC but also help decision makers make better decisions. At last, a real FMC as a case is presented to illustrate the proposed method. INDEX TERMS Flexible manufacturing cell, fault analysis, FTA-FMEA, TOPSIS, meta-action.

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“…main oil circuit fault F 2 operating system fault F 3 return oil circuit fault F 4 control system fault F 5 flow fault F 6 left flap fault F 7 right flap fault F 8 shell oil return fault F 9 drive component fault F 10 EDP flow fault F 11 EMP flow fault P k occurrence probability of the kth minimum path set P cj (i) operator state probability when the state value of jth operator is i P rj (i) output signal state probability when the output signal of jth operator is i Q k kth minimum path set T the fault of the aircraft flap hydraulic system q i occurrence probability of the ith bottom event X output signal of power module x i the ith bottom event Y the output signal of actuator module λ the fault rate…”

Section: Nomenclaturementioning

confidence: 99%

“…The GO methodology was first proposed by the US military in the 1960s as an effective method to analyze the reliability of weapon systems (7) . It is particularly suitable for the reliability analysis of multi-state complex systems containing actual logistics, such as current, airflow and liquid flow, and more suitable for the analysis of complex and time-series systems (8)(9)(10) . It can effectively avoid construction difficulties and poor modeling consistency when using FTA (11) method to analyze the complex system reliability.…”

Section: Introductionmentioning

confidence: 99%

Application of GO methodology in reliability analysis of aircraft flap hydraulic system

Duan

2019

Aeronaut. j.

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The Goal-Oriented methodology (GO methodology) is an effective method for the reliability analysis of complex systems. It is especially suitable for the reliability analysis of multi-state complex systems containing the actual logistics, such as current, airflow and liquid flow. In order to solve the limitation that the GO methodology is not suitable for the reliability analysis of the system with feedback loop, the Boolean algebra idea is introduced to construct the Boolean operation formula of the feedback loop. In this paper, a certain type of civil aircraft flap hydraulic system with feedback loop is taken as the research object. According to the structural schematic diagram of the flap hydraulic system, the GO model of the flap hydraulic system is established. Next, the GO calculation is carried out to obtain the reliability of the flap hydraulic system. The comparison between the system reliability without feedback loop and that with feedback loop proves that the GO methodology with feedback loop is more accurate. The reliability of the system is analyzed by using the fault tree analysis (FTA) method, and the GO methodology with feedback loop is compared with the FTA method to verify the availability and correctness of the GO methodology in the reliability analysis and safety evaluation of aircraft flap hydraulic system.

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Enhanced GO methodology to support failure mode, effects and criticality analysis

Cited by 20 publications

References 33 publications

The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

A Hybrid Multilevel FTA-FMEA Method for a Flexible Manufacturing Cell Based on Meta-Action and TOPSIS

Application of GO methodology in reliability analysis of aircraft flap hydraulic system

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