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 has not been systematically studied in the context of RCM. This article presents a framework of model-based RCM analysis, which is driven by functional modeling and reasoning. The study focuses on identifying the so-called maintenance significant items through assessment of failure consequences. Multilevel flow modeling (MFM) is proved competitive to identify sufficient system functions that expect maintenance to preserve. It is also able to define failure modes and represent their interactions with system functions, which are essential to RCM. A failure analysis tool for RCM is developed by taking advantage of the causal reasoning capability of MFM, which can be used to automatically analyze consequences of all failures predefined for a target system and generate equipment classifications useful to maintenance optimization. The study provides the possibility of RCM automation, which has been highly demanded by various complex industries. Moreover, as a fundamental functional knowledge framework, MFM can easily accommodate changes in design and operation, which affords the opportunity of implementing a living RCM program.

Automated summary

This article presents a framework of model-based RCM analysis driven by functional modeling and reasoning. The study focuses on identifying maintenance significant items through assessment of failure consequences and utilizes Multilevel flow modeling (MFM) to define failure modes and their interactions with system functions. A failure analysis tool for RCM is developed using the causal reasoning capability of MFM and can automatically analyze consequences of all predefined failures and generate equipment classifications for maintenance optimization. The study provides the possibility of RCM automation and the opportunity of implementing a dynamic RCM program with MFM.