Integrating condition-based monitoring with process sensors information for operation and maintenance in a functional modeling framework

Safe operations and adequate maintenance are two main means to achieve reliable production and reduce downtime of a plant. While the tasks of operations and maintenance are carried out by two different groups of staff, as a result, the close relationship between the two tasks is split. In this paper, this challenge is handled by a proposed integrated functional modeling framework. In this framework, the Multilevel Flow Modeling (MFM) method with its cause-consequence reasoning rules is used. Condition-based monitoring is a well-accepted strategy for predictive maintenance and fault detection based on measurements is a well-developed technology for operation support. Information fusion including monitoring conditions of assets and process sensors information for both operation and maintenance in the same modeling framework is desired. The qualitative relationship distribution between operations and maintenance can be established based on the function states of the system. In addition, these relationships are visible for both groups of staff. As a result, the detected information in the early stage of the development of the unpleasant scenarios is used to improve their situation awareness, so that the undesired emergency shutdown from both perspectives of operation and maintenance is prevented. Consequently, it can reduce production loss. A case study of operations and maintenance of a seawater injection system is carried out and shows the industrial applicability of the proposed framework. The case study strongly reveals that there is a highly close relation between operation and maintenance for ensuring the system working properly. It demonstrates that the proposed integrated framework is not only able to support operational tasks but also for the maintenance tasks by including relevant maintenance information of the system. The results show that it can potentially help with decreasing downtime of the system.