# PAL-VMEA: A Novel Method for Enhancing Decision-Making Consistency in Maintenance Management
###### 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. P 2020 10 8040 10.3390/app10228040 10.  
preventive and predictive maintenance to avoid failure occurrence [1; 2; 3]. As an evolution of RCM, Risk-Based Maintenance (RBM) was proposed in the 2000s and has attracted a significant attention in the process and offshore industries, since it provides a cost-effective tool to reduce the probability of failure in the critical components and associated consequences. The main goal of the methodology is to define the most critical scenarios in terms of risk and to select as most critical components the ones that are part of those critical scenarios [4; 5]. Coupled to these methodologies, Decision-Making Process (DMP) is used to improve the prioritization of pieces of equipment with regard to maintenance planning. Besides risk and reliability factors, as prospected by the previously mentioned methods, maintenance investment, business interruption loss, and maintenance technique feasibility can be included as decision variables. Accordingly, the design and implementation of maintenance plans becomes a complex problem, involving material, economic, social, and individual aspects [6]. Thus, a successful implementation requires considering the opinion of different experts in a context of uncertainty and incomplete and inaccurate data [7]. For example, the uncertainty of the RCM or Risk-Based Inspection (RBI) analysis is associated with expert judgment used to elaborate FMEA or other risk analysis techniques that use a numerical classification system to express probability of occurrence and consequences of system's components failure modes. It is hard to reach a consensus when dealing with a great number of experts with different backgrounds [8]. Several decision support systems have been developed to improve the determination of optimal maintenance plans [9; 10; 11; 12], maintenance scheduling [13; 14], and maintenance workload [15; 16], as well as the determination of criticality levels of pieces of equipment [17; 18; 19]. Generally, such criticalities are individually established, without explicitly considering potential differences between persons involved in the analysis or areas interested in the problem [20]. In case of inexistence of consensus between the experts, the opinions should be weighted through the use of weighting factors that ideally must take into consideration the appropriateness and relevance of the expert in the field under analysis [21]. Percentiles can be used to combine expert opinions and arithmetic and geometric averages can be used, although the results are dependent on the number of experts invited for the elicitation.