Modular Plant Maintenance Scheduling Using Digital Module Data
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Solution Overview
Problem
The maintenance needs of modular plants, assembled from reused or refurbished modules, are hard to predict, leading to operational availability risks due to uncertainties in module health conditions, which complicates preventive and predictive maintenance planning.
Innovation Solution
An automated method generates a maintenance schedule by receiving planning data, suggesting suitable modules based on maintenance information, determining expected maintenance needs, and validating the schedule against production targets before assembly, using digital modules and a CMMS to automate predictive and preventive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If modules are reused or refurbished to assemble modular plants, then asset optimization and flexible production are improved, but maintenance need prediction becomes difficult leading to operational availability risk
Solution Approach 1:
The system performs preliminary maintenance scheduling during the plant design phase before the modular plant is assembled and operational. By determining expected maintenance needs and creating maintenance schedules in advance using digital modules and planning data, the system eliminates the uncertainty that would otherwise exist when modules are first put into service, thereby ensuring operational availability from day one while maintaining flexible production capabilities
Solution Approach 2:
The system uses digital modules as virtual copies of physical modules to perform maintenance planning. These digital modules contain all necessary maintenance information and can be manipulated, selected, and validated without affecting the actual physical modules. This copying approach allows comprehensive maintenance scheduling and validation before assembly, resolving the contradiction between using reused modules for flexibility and predicting their maintenance needs for reliability
2Reliability
If preventive maintenance planning is performed manually in traditional plants, then maintenance coverage is achieved, but planning effort and time consumption increase
Solution Approach 1:
The system replaces manual maintenance planning with an automated computer-based process. The control unit automatically receives planning data, selects appropriate digital modules, determines maintenance needs, and generates maintenance schedules without manual intervention. This substitution of mechanical/manual planning with automated computational processes maintains comprehensive maintenance coverage while dramatically reducing planning time and effort
Solution Approach 2:
The system enables self-service maintenance planning where the control unit autonomously performs the entire maintenance scheduling process. By using planning data, module information, and validation rules, the system independently generates and validates maintenance schedules without requiring manual planning effort, achieving both comprehensive maintenance coverage and time efficiency
3Measurement precision
If predictive maintenance is implemented requiring knowledge of all devices and process topology, then maintenance precision is improved, but system complexity and integration effort increase
Solution Approach 1:
The system segments the maintenance planning task by working with individual digital modules rather than requiring comprehensive knowledge of the entire plant system. Each digital module contains self-contained maintenance information that can be independently processed. This segmentation allows precise maintenance planning for each module while avoiding the need to integrate complex system-wide models, thereby maintaining maintenance precision without increasing integration effort
Data Source
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AI summary
The present invention is concerned with an automated maintenance schedule generation method, comprising the steps: receiving (S1) planning data for designing a modular plant comprising physical modules (20); selecting (S2) digital modules (40), correlating to the respective physical modules (20), from a database (30), depending on the received planning data (Dp), wherein the digital modules (40) comprise maintenance information (I) of the respective correlating physical modules (20); associating (S3) the selected digital modules (40) depending on the received planning data (Dp); determining (S4) an expected maintenance need (N) comprising an expected maintenance issue and/or an expected maintenance issue date for the selected physical (20) modules depending on the received maintenance information (I); and generating (S5) a maintenance schedule (S) for the modular plant depending on the determined maintenance need (N).