A New Model for Failure Mode and Effects Analysis Based on <i>k</i>-Means Clustering Within Hesitant Linguistic Environment

Duan, Chunyan; Chen, Xu-Qi; Shi, Hua; Liu, Hu‐Chen

doi:10.1109/tem.2019.2937579

Cited by 62 publications

(22 citation statements)

References 54 publications

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“…On the other hand, computing with words (CW) was proposed by [23] and the 2-tuple linguistic representation model was initiated by [24]. A large number of extended methods based on the 2-tuple linguistic model have been developed to a notable degree [9,25,26]; probabilistic hesitant fuzzy language was presented to solve the problem of DMs hesitating between multiple options in the evaluation process [27]; linguistic distribution assessments can enable DMs to better reflect their actual experience and avoid information loss and distortion [9,25]; double hierarchy hesitant fuzzy linguistic term sets allow DMs to evaluate problems and solutions using a much more intuitive expression method [13].…”

Section: Related Workmentioning

confidence: 99%

“…Different from general risk evaluation methods that analyze problems after an adverse event occurs, FMEA is a tool for proactive risk assessment and management, evaluating and eliminating failures before they occur or reach customers [9,10]. Therefore, it has been widely utilized in various practical scenarios, such as cold-chain logistics management [11], healthcare services [12], energy issues [13,14], semiconductor manufacturing [15], etc. Usually, a classic FMEA model includes the following four steps: (1) failure modes (FMs) and their causes and results are identified; (2) the risk priorities of FMs are determined by risk priority numbers (RPNs), which involves three risk factors: the probability of occurrence (O), the severity of effects (S), and the difficulty of detection (D); (3) the risk priority ranking of FMs is obtained, such that the FMs with larger RPN values would cause severer problems, requiring higher priorities; (4) corresponding measures are taken for high-risk issues [10,16,17].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

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A Calibrated Individual Semantic Based Failure Mode and Effect Analysis and Its Application in Industrial Internet Platform

Chen

Liu

et al. 2022

Mathematics

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This article proposes a calibrated individual semantic (CIS)-based failure mode and effect analysis (FMEA) to deal with the risk evaluation of industrial internet platforms (IIP) from four perspectives: network security, data processing capability, equipment performance, and openness. The novelty of the CIS model is based on the deviation between linguistic terms and numerical values to calibrate linguistic scales of decision-makers (DMs). Not only can it handle situations in which different DMs have different understandings of the same term, but it is also suitable for multiple attributes decision-making with uncertainty. In addition, this new FMEA framework considers the consensus-reaching process as a way to eliminate the disagreement among DMs from different departments. Finally, a comparison between the proposed and traditional method is presented to illustrate the advantages of new method.

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Section: Related Workmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

A Calibrated Individual Semantic Based Failure Mode and Effect Analysis and Its Application in Industrial Internet Platform

Chen

Liu

et al. 2022

Mathematics

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“…It is evaluated in the context of farming applications. Duan et al [28] analyze evaluations of failure modes in natural language by FMEA experts, using fuzzy sets to extract features, and the kmeans algorithm to cluster the failure modes. Xu et al [29] proposed a method to construct the component-failure mode (CF) matrix automatically, by mining unstructured texts using the Apriori algorithm and the semantic dictionary WordNet to build a standard set of failure modes.…”

Section: Related Workmentioning

confidence: 99%

Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning

Cotroneo,

De Simone,

Liguori

et al. 2021

Preprint

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Identifying the failure modes of cloud computing systems is a difficult and time-consuming task, due to the growing complexity of such systems, and the large volume and noisiness of failure data. This paper presents a novel approach for analyzing failure data from cloud systems, in order to relieve human analysts from manually fine-tuning the data for feature engineering. The approach leverages Deep Embedded Clustering (DEC), a family of unsupervised clustering algorithms based on deep learning, which uses an autoencoder to optimize data dimensionality and inter-cluster variance. We applied the approach in the context of the OpenStack cloud computing platform, both on the raw failure data and in combination with an anomaly detection pre-processing algorithm. The results show that the performance of the proposed approach, in terms of purity of clusters, is comparable to, or in some cases even better than manually fine-tuned clustering, thus avoiding the need for deep domain knowledge and reducing the effort to perform the analysis. In all cases, the proposed approach provides better performance than unsupervised clustering when no feature engineering is applied to the data. Moreover, the distribution of failure modes from the proposed approach is closer to the actual frequency of the failure modes.

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“…In addition, decision makers can utilize DHHLTSs to express their opinions comprehensively. Given these advantages, many researchers have adopted the DHHLTSs to handle complex uncertain linguistic information in various decision-making problems [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A New Integrated Multi-Criteria Decision Making and Multi-Objective Programming Model for Sustainable Supplier Selection and Order Allocation

You

Zhang

et al. 2020

Symmetry

Self Cite

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With the increasing pressure from global competition, manufacturers have realized that sustainable production is significant in supply chain management. Sustainable supplier selection and order allocation (SSS&OA) play a distinct and critical role for organizations to achieve sustainable development and build competitive advantage. In this paper, we aim to develop a novel SSS&OA model for selecting the most suitable sustainable suppliers and determining the optimal order sizes among them. First, double hierarchy hesitant linguistic term sets (DHHLTSs) are adopted to deal with uncertainty in evaluating the sustainable performance of alternative suppliers. Then, an extended decision field theory is proposed to choose efficient sustainable suppliers dynamically. Considering quantity discount, a multi-objective linear programming (MOLP) model is established to allocate reasonable order quantities among the selected suppliers. Finally, the applicability and effectiveness of the developed model are illustrated through its application in the electronic industry and through a comparative analysis with other methods.

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A New Model for Failure Mode and Effects Analysis Based on k-Means Clustering Within Hesitant Linguistic Environment

Cited by 62 publications

References 54 publications

A Calibrated Individual Semantic Based Failure Mode and Effect Analysis and Its Application in Industrial Internet Platform

A Calibrated Individual Semantic Based Failure Mode and Effect Analysis and Its Application in Industrial Internet Platform

Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep Learning

A New Integrated Multi-Criteria Decision Making and Multi-Objective Programming Model for Sustainable Supplier Selection and Order Allocation

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