Abstract
Predictive maintenance (PdM) or Prognostics and Health Management (PHM) assists in better predicting the future state of physical assets and making timely and better-informed maintenance decisions. Many companies nowadays desire the implementation of such an advanced maintenance policy. However, the first step in any implementation of PdM is identifying the most suitable candidates (i.e. systems, components). This is to assess where PdM would provide the greatest benefit in performance and costs of downtime. Although multiple selection methods are available, these methods do not always lead to the most suitable candidates for PdM. The main reason is that these methods mainly focus on critical components without considering the clustering of maintenance, and the technical, economic, and organizational feasibility.This paper proposes a three-stage funnel-based selection method to enhance this process. The first step of the funnel helps to significantly reduce the number of suitable systems or components by a traditional filtering on failure frequency and impact on the firm. In the second and third step, a more in-depth analysis on the remaining candidates is conducted. These steps help to filter potential showstoppers and study the technical and economic feasibility of developing a specific PdM approach for the selected candidates. Finally, the proposed method is successfully demonstrated using two distinct cases: a vessel propulsion system and a canal lock.
Highlights
Preventing unexpected failures from occurring is important for many complex systems such as production systems, medical equipment and high-tech products
This paper aimed to propose a method to select the suitable components for Predictive maintenance (PdM) / Prognostics and Health Management (PHM) within an asset
It can be concluded that the proposed three stage approach can be widely applied for suitable candidate selection, as was demonstrated by the canal lock (RWS) and naval vessel propulsion system (MAR) cases
Summary
Preventing unexpected failures from occurring is important for many complex systems such as production systems, medical equipment and high-tech products. Executives in such asset-intensive industries often regard unexpected failures of their physical assets as the primary operational risk to their business Such unexpected downtime can be disruptive in complex manufacturing supply chains and imposes high costs due to forgone productivity (LaRiviere et al, 2016). Predictive maintenance (PdM) techniques inform the asset owner or operator about the current and preferably future state of their assets. PdM thereby helps to reduce unexpected failures, improve the reliability and dependability of equipment and prevent unnecessary replacement of components. System level monitoring can be used to get control and system performance data
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