Abstract
Decision-making regarding maintenance planning has become increasingly critical. In view of the need for more assertive decisions, methods, and tools based on failure analysis, performance indicators, and risk analysis have obtained great visibility. One of these methods, the Variation and Mode Effect Analysis (VMEA), is a statistically based method that analyses the effect of different sources of variations on a system. One great advantage of VMEA is to facilitate the understanding of these variations and to highlight the system areas in which improvement efforts should be directed. However, like many knowledge-based methods, the inherent epistemic uncertainty can be propagated to its result, influencing following decisions. To minimize this issue, this work proposes the novel combination of VMEA with Paraconsistent Annotated Logic (PAL), a technique that withdraws the principle of noncontradiction, allowing better decision-making when contradictory opinions are present. To demonstrate the method applicability, a case study analyzing a hydrogenerator components is presented. Results show how the proposed method is capable of indicating which are the failure modes that most affect the analyzed system, as well as which variables must be monitored so that the symptoms related to each failure mode can be observed, helping in decision-making regarding maintenance planning.
Highlights
In view of the need for assertive decisions in asset and maintenance management, methods and tools based on failure analysis, performance indicators, and risk analysis have obtained great visibility in industrial processes, leading to more consistent decision-making
The use of Variation and Mode Effect Analysis (VMEA) is proposed to analyze the condition of a system and, in this process of adapting the method to a new context related to maintenance, Key Product Characteristics (KPC) are translated as the main functions of the analyzed subsystems, as Sub-KPCs are understood as the flows that integrate the components of these subsystems in the fulfillment of their functions
The Paraconsistent Annotated Logic (PAL)-VMEA method combined with the Best-Worst Method (BWM) method, presented in this work, is a variation of the VMEA method with the inclusion of a mechanism for assessing the epistemic uncertainty inherent in knowledge-based methods, such as the Failure Mode and Effects Analysis (FMEA) itself and its offspring
Summary
In view of the need for assertive decisions in asset and maintenance management, methods and tools based on failure analysis, performance indicators, and risk analysis have obtained great visibility in industrial processes, leading to more consistent decision-making. Despite the favorable performance of both methods in the aforementioned areas, the lack of application of such techniques in maintenance and reliability engineering decision-making processes, added to the difficulty in assertive decision-making in maintenance activities associated with infrastructure systems, such as power generation and petrochemical plants, are motivating factors for this research In this way, the present work aims to adapt the VMEA method to analyze the sensitivity of the KPCs of the main items of an equipment, according to the potential and functional failure modes, which are considered as NFs. Due to the importance of the weight assignment process in the VMEA analysis, the Paraconsistent Bi-Annotated Logic (PAL2v)—Paraconsistent Annotated Logic with annotation of two values—is applied as a weight assignment tool for the variables involved in this analysis, which make up the indicators that measure the impact of failure modes on the variation of the key characteristics (main function) of the components of an equipment. Mainly practical case studies, present results that consider the inconsistencies For this reason, they are more propitious in framing problems caused by situations of contradictions, ideal for applying a nonclassical logic such as PAL in the decision-making process. BWM is an easy-to-apply method for determining the most appropriate weights for decision criteria, with guaranteed reliability of results, as it performs a consistency check of the judgments (even comparison between the criteria) made by the decision-makers
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