Critical ranking of steam handling unit using integrated cloud model and extended PROMETHEE for maintenance purpose

Panwar, Nitin; Kumar, Sanjay

doi:10.1007/s40747-020-00210-y

Cited by 8 publications

(3 citation statements)

References 49 publications

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“…Virtual reality (VR) technology was adopted for identifying potential hazards in [11]. The cloud model and preference ranking organization method for enrichment evaluation (PROMETHEE) were used in [12] to rank failure causes based on considerations such as downtime duration, spare part criticality, and safety-related risks. Furthermore, the degree of uncertainty and divergence degree were used to calculate the primary and secondary weights of decision-makers, respectively.…”

Section: Literature Review 21 Fmea and Mcdm Methodsmentioning

confidence: 99%

Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems

Chnina,

Daneshvar

2024

Applied Sciences

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The failure mode causes and effects analysis (FMCEA) is a commonly used reliability approach. It identifies, predicts, and analyzes potential failure modes affecting the proper function of equipment or the process under study, along with their roots and consequences. FMCEA aims to evaluate and assess the risks resulting from their occurrence, intending to suggest corresponding repair, adjustment, and precautionary measures to be planned during the conception, instruction, or implementation stages. However, the FMCEA has been criticized in the literature for its many inherent shortcomings related to risk assessment and prioritization. Therefore, this study presents an enhanced FMCEA method to address the deficiencies of the traditional risk priority number (RPN) and improve the reliability of risk assessments and corrective actions. A data envelopment analysis (DEA), as a non-parametric method, is used to evaluate the efficiency of these failures by considering their fixing time and cost and deciding on their final priority ranks. Sub-failure modes and their interrelationships are also taken into account. The radio frequency identification (RFID) system was chosen as an example due to its core role in Industry 4.0 and the Internet of Things (IoT) to demonstrate the effectiveness and usefulness of the proposed method. A total of 67 failures related to both hardware and software parts, including the environmental impacts of this technology, have been disclosed. The results of the conventional and the suggested FMCEA methods are found to be considerably different, with ten failure modes classified as being the most efficient.

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Section: Literature Review 21 Fmea and Mcdm Methodsmentioning

confidence: 99%

Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems

Chnina,

Daneshvar

2024

Applied Sciences

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“…FMEA is an analytical method to identify and prioritize potential failures, causes and effects of them, and to provide the preventive actions in a company. [2][3][4] The main tool of FMEA for risk evaluation and prioritization of every failure mode is Risk Priority Number (RPN) score, and it is computed as the product of values of three risk factors; Detection (D), Occurrence (O), and Severity (S). 5 FMEA has been successfully applied many areas such as military, manufacturing, healthcare, etc.…”

Section: Introductionmentioning

confidence: 99%

ARAS method based on Z‐numbers in FMEA

Adalı

Tuş

2023

Quality & Reliability Eng

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Failure Mode and Effects Analysis (FMEA) is one of the risk assessment methods, and is widely applied to the various areas in real life. The method has two main phases. In the first phase, the potential failure modes of products, processes, systems, and services are determined, whereas in the second phase, the risk assessment and prioritization of these failure modes are made, and preventive and corrective actions are taken to prevent the emergence of failure modes with relatively higher risk. Although the method is widely used over the years, researchers have used FMEA together with different theories and methods to overcome several shortcomings of the method. In this study, a new method, Z‐ARAS (Additive Ratio ASsessment), is proposed on a risk analysis in FMEA to eliminate uncertainty information in decision‐making practice. This method is the modification of ARAS method by integrating with Z‐numbers. Z‐numbers are performed to reflect the reliability of decision makers’ evaluations. A real case covering a cable company's production failures that is adopted from the literature is solved to demonstrate the applicability of the proposed method. A sensitivity analysis is performed, and also a comparative analysis is presented with Z‐MARCOS (Measurement Alternatives and Ranking according to the COmpromise Solution) and Z‐CODAS (COmbinative Distance‐based ASsessment) methods to highlight the effectiveness of Z‐ARAS method. It can be concluded that Z‐ARAS is an alternative method to detect and analyze critical failure modes, and assist decision makers in implementing a reliable preventive strategy.

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“…In this study, the cloud model (proposed by Li et al [24]) was selected as it better reflects the vagueness and randomness of the concept of uncertainty. Cloud models have been applied in many fields [25][26][27][28][29] due to the randomness and stability. The main advancement of this study was the successful integration of a cloud model with DEMATEL−ISM as a powerful analytic tool.…”

Section: Introductionmentioning

confidence: 99%

The Multi-Level Hierarchical Structure of the Enablers for Supply Chain Resilience Using Cloud Model-DEMATEL–ISM Method

Chen

Huang

2022

Sustainability

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Companies must shift from traditional supply chain management thinking to addressing or preventing increases in vulnerability, uncertainty, and unforeseen supply chain disruptions facing complex global supply chains. Systems with a large number of elements may be susceptible to nonlinear interactions, perturbation of which may lead to serious impacts. Thus, there is an increasing need to determine the importance of individual elements and how these elements interact. Published studies of supply chain resilience (SCRes) do not clearly determine the hierarchical structure of factors, and the understanding of interactions between factors remains fragmented. In this study, we proposed a cloud model-DEMATEL−ISM method to overcome the disadvantages of traditional DEMATEL−ISM integration methods. The MICMAC method (cross-impact matrix multiplication applied to classification) was also used to classify the enablers of SCRes based on driving force and dependence force. We tested these approaches by studying the new energy vehicle industry in China. The results suggest that companies trying to strengthen SCRes should focus on enablers at the base layer with a high driving force, particularly the enablers of social capital, restructuring, risk management culture, information technology application, trust and collaboration, information sharing, and learning capability.

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Critical ranking of steam handling unit using integrated cloud model and extended PROMETHEE for maintenance purpose

Cited by 8 publications

References 49 publications

Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems

Aggregation of Risk Management and Non-Parametric Models to Rank Failure Modes of Radio Frequency Identification Systems

ARAS method based on Z‐numbers in FMEA

The Multi-Level Hierarchical Structure of the Enablers for Supply Chain Resilience Using Cloud Model-DEMATEL–ISM Method

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