Automatic Identification of Maintenance Significant Items in Reliability Centered Maintenance Analysis by Using Functional Modeling and Reasoning
Complex industrial systems adopt reliability centered maintenance (RCM) for maintenance optimization to improve safety and reduce maintenance cost. Preserving function is the core maintenance principle, yet the function concept itself has not been systematically studied in the context of RCM. This article presents a framework of model-based RCM analysis that is driven by functional modeling, which serves as a medium to formalize the functional knowledge essential for the RCM analysis. The use of functional reasoning for failure analysis provides the possibility of RCM automation. The study focuses on identifying the so-called maintenance significant items through assessment of failure consequences. Multilevel flow modeling (MFM) is applied to identify system functions desired to be preserved by maintenance, as well as equipment functions necessary for defining failure modes, and their interactions. A failure analysis tool is developed based on MFM's causal reasoning capability, which can be used to automatically analyze consequences of all defined equipment failures in a target system and generate equipment classifications useful to maintenance optimization. An example has been used to demonstrate the proposed approach. As a fundamental functional knowledge framework, MFM can easily handle changes in design and operation, which affords the opportunity of implementing a living RCM program
Year of publication: |
[2023]
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Authors: | Song, Mengchu ; Zhang, Xinxin ; Lind, Morten |
Publisher: |
[S.l.] : SSRN |
Saved in:
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