Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system

Rafie, Meraj; Namin, Farhad Samimi

doi:10.1016/j.ijmst.2015.05.021

Cited by 84 publications

(37 citation statements)

References 23 publications

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“…Therefore, a GT2FS can be regarded as a collection of a high number of IT2FLSs. Meanwhile, Liu [10] used only 5 to 10 α-planes and demonstrated that the required accuracy of centroid calculation is achieved. The new architecture for the GT2FS based on the α-plane representation is illustrated in Figure 4 [23].…”

Section: General Type-2 Fuzzy Systemsmentioning

confidence: 99%

A Novel Type-2 Fuzzy Logic for Improved Risk Analysis of Proton Exchange Membrane Fuel Cells in Marine Power Systems Application

et al. 2018

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A marine energy system, which is fundamentally not paired with electric grids, should work for an extended period with high reliability. To put it in another way, by employing electrical utilities on a ship, the electrical power demand has been increasing in recent years. Besides, fuel cells in marine power generation may reduce the loss of energy and weight in long cables and provide a platform such that each piece of marine equipment is supplied with its own isolated wire connection. Hence, fuel cells can be promising power generation equipment in the marine industry. Besides, failure modes and effects analysis (FMEA) is widely accepted throughout the industry as a valuable tool for identifying, ranking, and mitigating risks. The FMEA process can help to design safe hydrogen fueling stations. In this paper, a robust FMEA has been developed to identify the potentially hazardous conditions of the marine propulsion system by considering a general type-2 fuzzy logic set. The general type-2 fuzzy system is decomposed of several interval type-2 fuzzy logic systems to reduce the inherent highly computational burden of the general type-2 fuzzy systems. Linguistic rules are directly incorporated into the fuzzy system. Finally, the results demonstrate the success and effectiveness of the proposed approach in computing the risk priority number as compared to state-of-the-art methods.

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Section: General Type-2 Fuzzy Systemsmentioning

confidence: 99%

A Novel Type-2 Fuzzy Logic for Improved Risk Analysis of Proton Exchange Membrane Fuel Cells in Marine Power Systems Application

et al. 2018

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“…Generally, prediction of subsidence susceptibility zones requires the input of several environmental factors and the application of perdition models [5]. Several previous studies have developed quantitative and qualitative models that have been successfully applied in various hazard susceptibility zones worldwide [3][4][5][6][7][8][9][10][11]. These include logistic regression (LR) [3], frequency ratio (FR) [3,6], weight of evidence (WOE) [3], evidential belief function (EBF) [4], artificial neural network (ANN) [3,5,7,8], support vector machine (SVM) [9], random forest (RF) [10], and fuzzy logic (FL) [8,11] models.…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

Syifa

Lee

et al. 2019

Applied Sciences

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To effectively prevent land subsidence over abandoned coal mines, it is necessary to quantitatively identify vulnerable areas. In this study, we evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility (LSS) maps. All models were trained using half of a land subsidence inventory, and validated using the other half of the dataset. The model performance was evaluated by comparing the area under the receiver operating characteristic (ROC) curve of the resulting LSS map for each model. Among all models tested, the logit boost, which is a meta-ensemble machine leaning model, generated LSS maps with the highest accuracy (91.44%), i.e., higher than that of the other Bayesian and functional machine learning models, including the Bayes net (86.42%), naïve Bayes (85.39%), logistic (88.92%), and multilayer perceptron models (86.76%). The LSS maps produced in this study can be used to mitigate subsidence risk for people and important facilities within the study area, and as a foundation for further studies in other regions.

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“…Emovon [3] proposed a ranking method combining VIKOR algorithm and the compromise-based mean algorithm, which can not suitably represent uncertain information. Rafie [4] proposed a fuzzy FMEA method, in which the severity degree and detection degree are obtained by fuzzy rules, as the occurrence degree is trained by artificial neural network, so this method needs a large number of samples.…”

Section: Introductionmentioning

confidence: 99%

A Risk Assessment Method Based on OWGA Operator and Fuzzy DEMATEL Algorithm

Lin¹,

Jia²

2016

Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)

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Keywords: failure mode and effect analysis (FMEA); risk assessment; triangular fuzzy number; ordered weighted geometric averaging operator; decision making trial and evaluation method (DEMATEL) Abstract. For the deficiency of traditional failure mode and effects analysis ( FMEA) in solving risk ranking problem for related failure modes, this paper proposed a risk assessment method based on ordered weighted geometric averaging (OWGA) operator and fuzzy decision making trial and evaluation method (DEMATEL). Firstly, fuzzy evaluation of failure modes was carried out which is aggregated by OWGA operator, and the aggregated value was taken as the influence degree of failure cause effect on corresponding failure mode. Then, the initial direct influence matrix was constructed by DEMATEL method, normalize this matrix to compute the total influence matrix. According to the total influence matrix, the relation degree of each failure cause was calculated, based on which the failure risk assessment of the system can be carried out . The proposed method was applied in FMEA risk analysis of basic parts of metro car door system, the feasibility and effectiveness of the proposed method is verified by comparing with the results of the RPN method.

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Prediction of subsidence risk by FMEA using artificial neural network and fuzzy inference system

Cited by 84 publications

References 23 publications

A Novel Type-2 Fuzzy Logic for Improved Risk Analysis of Proton Exchange Membrane Fuel Cells in Marine Power Systems Application

A Novel Type-2 Fuzzy Logic for Improved Risk Analysis of Proton Exchange Membrane Fuel Cells in Marine Power Systems Application

Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models

A Risk Assessment Method Based on OWGA Operator and Fuzzy DEMATEL Algorithm

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