Predictive Equipment Control Using Abnormality Simulation
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Solution Overview
Problem
Existing control systems for equipment in plants rely on predictive models and worker intuition to avoid equipment abnormalities, lacking an efficient method to adjust control parameters proactively.
Innovation Solution
A control support apparatus that estimates equipment abnormalities using measurement data, simulates future states of the equipment under different candidate control methods, and selects an optimal control method to prevent abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workers use their experience and intuition to adjust control parameters, then equipment abnormalities can be avoided, but the control process lacks objectivity and efficiency
Solution Approach 1:
The patent replaces the mechanical reliance on worker experience and intuition with an automated information processing system. The control support apparatus uses measurement data, predictive models, and simulation results to objectively determine optimal control parameters, substituting human cognitive processes with computational algorithms that can analyze multiple variables simultaneously and provide data-driven recommendations.
Solution Approach 2:
The system enables self-service by automatically generating control parameter recommendations based on real-time measurement data and predictive modeling. The control support apparatus autonomously analyzes equipment state, simulates future scenarios, and provides adjusted control parameters without requiring worker intervention, allowing the control system to self-optimize based on observed patterns and predictions.
2Reliability
If predictive models are used to estimate equipment state, then early warning of abnormalities is possible, but proactive adjustment of control parameters is not achieved
Solution Approach 1:
The patent implements preliminary action by simulating future equipment states under different control scenarios before actual abnormalities occur. The control support apparatus uses the predictive model to forecast equipment behavior and tests multiple candidate control methods in advance through simulation, selecting the optimal control adjustments before the equipment actually enters an abnormal state, thereby preventing rather than just detecting problems.
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where measurement data from actual equipment operation feeds into the predictive model, which then generates simulation results that inform control parameter adjustments. The simulation outcomes are fed back to refine control decisions, creating a continuous cycle where prediction results directly influence control actions, and updated measurement data further improves future predictions.
3Measurement precision
If multiple candidate control methods are simulated, then optimal control selection is improved, but simulation time and computational resources increase
Solution Approach 1:
The patent applies partial action by simulating only the necessary number of candidate control methods required to identify the optimal solution. Rather than exhaustively testing all possible control variations, the system generates a focused set of candidate methods based on the predictive model's identification of critical factors, simulates these targeted candidates, and selects the best option, achieving sufficient precision without unnecessary computational overhead.
Data Source
AI summary
Provided is a control support apparatus, comprising: an equipment abnormality estimation section for estimating an abnormality of equipment based on measurement data by measuring the equipment; a simulation section for simulating a future state of the equipment when controlling the equipment by each of a plurality of candidate control methods according to an abnormality estimation result of the equipment; and a simulation abnormality estimation section for estimating a future abnormality of the equipment based on a future state of the equipment for each of the plurality of candidate control methods.