Integrated Production and Maintenance Planning With State Feedback
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Solution Overview
Problem
Current production and maintenance planning systems for raw materials industry plants rely on manual inputs and empirical values, leading to suboptimal adaptations due to unforeseen disturbances, and lack automated integration of actual component states and production data, resulting in inefficient production and maintenance processes.
Innovation Solution
A method that integrates a state monitoring system to predict future component states using production planning data, a quality determination system to assess output product states, and a maintenance planning system that considers both previous and future states, enabling automated and optimized production and maintenance planning by coordinating with production planning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual inputs and empirical values are used for production and maintenance planning, then the planning process is simple to implement, but the planning accuracy and adaptability to unforeseen disturbances deteriorate
Solution Approach 1:
The system implements feedback loops where actual component states measured by sensor devices are continuously fed back to the planning systems. The production planning system receives feedback on actual production states, and the maintenance planning system receives feedback on actual component conditions, enabling automatic adaptation of plans based on real-time data rather than relying on manual inputs and empirical values
Solution Approach 2:
The planning systems perform self-service by automatically generating and adapting production and maintenance plans based on received data from sensor devices and other systems. The systems use algorithms to independently determine optimal planning decisions without requiring manual intervention, thereby improving planning accuracy while maintaining ease of implementation through automated processes
2Device complexity
If production and maintenance planning are performed separately, then each planning process is simpler and more focused, but the overall system coordination and productivity deteriorate
Solution Approach 1:
The system merges production planning and maintenance planning into an integrated framework where both plans are coordinated through shared data exchange. The production planning system and maintenance planning system communicate through defined interfaces, allowing them to consider each other's constraints and objectives when generating plans, thereby improving overall system productivity while maintaining manageable complexity through modular architecture
Solution Approach 2:
The planning systems are designed with multi-functionality to handle both production and maintenance planning tasks. The systems can switch between different planning modes and accommodate various planning scenarios, enabling coordinated optimization of both production and maintenance activities within a unified framework that improves overall system efficiency
3Device complexity
If actual component states and production data are not integrated into planning systems, then the planning systems are simpler and more stable, but the adaptability to real-time conditions and planning robustness deteriorate
Solution Approach 1:
The planning systems implement feedback mechanisms that continuously receive actual component states from sensor devices and production data from the automation system. This feedback enables the systems to adapt their plans in real-time based on actual conditions, improving robustness without requiring overly complex system architecture through standardized data interfaces and processing algorithms
Solution Approach 2:
The system performs preliminary actions by predicting future component states based on current trends and historical data. The maintenance planning system uses predicted future states to proactively schedule maintenance activities before components actually fail, enabling the system to adapt to real-time conditions while maintaining manageable complexity through predictive analytics rather than reactive complexity
Data Source
AI summary
A production planning system (6) for a raw materials industry plant (ANL), which determines the production planning data (Pi) thereof and specifies said data to the automation system (1) of the plant (ANL). A state monitoring system (7) determines previous and future anticipated states (Z1) of components of the plant (ANL). A quality determination system (8) determines states (Z2) of output products (Ai) produced and still to be produced by the plant (ANL) and/or past and future states (Z3) of the plant (ANL) as a whole. A maintenance planning system (9) and/or the production planning system (6) receive, from the state monitoring system (7), the states (Z1) of the components of the plant (ANL), determined by the state monitoring system (7) and, from the quality determination system (8), the states (Z2 and Z3) of the output products (Ai) and/or of the plant (ANL) as a whole, determined by the quality determination system (8). They consider the data received from the state monitoring system (7) and from the quality determination system (8) in the determination of maintenance planning data (W) and/or the production planning data (Pi).