Sensor-Driven Peripheral Machine Control for Factory Energy Load
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Solution Overview
Problem
Existing industrial control systems for manufacturing facilities require specialized knowledge and are difficult to update, leading to inefficient energy usage and resource management due to the need for custom-made solutions that are challenging to maintain with changes in manufacturing processes or equipment.
Innovation Solution
A machine learning-based control system that uses sensor data to train a model for real-time control of peripheral machines, optimizing energy consumption while ensuring energy demand is met, without requiring specialized knowledge of the manufacturing facility or process, and can be updated automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If custom-made control solutions are implemented for each factory, then the system can be tailored to specific manufacturing processes and machines, but the system becomes difficult to update and maintain when changes occur in the manufacturing facility
Solution Approach 1:
The control system automatically adapts to changes in the manufacturing facility by using machine learning algorithms that continuously learn from sensor data. The system self-updates its control strategies without requiring manual reconfiguration by experts, thereby maintaining adaptability while reducing maintenance complexity.
Solution Approach 2:
The system transitions from static custom-made control parameters to dynamic adaptive control that automatically adjusts to changing manufacturing conditions. The control model is continuously refined based on real-time sensor data, allowing the system to adapt to new machines, processes, or production requirements without manual intervention.
2Use of energy by moving object
If expert knowledge is required to design and implement control solutions, then the system can be optimized for energy efficiency, but the system requires specialized knowledge that is difficult to acquire and maintain
Solution Approach 1:
The patent replaces manual expert configuration with automated machine learning algorithms. The system uses sensors to collect data and automatically trains control models without requiring expert intervention, thereby achieving energy efficiency optimization while eliminating the need for specialized knowledge in system operation and maintenance.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the manufacturing facility is constantly monitored and fed back to the control model. This feedback mechanism allows the system to automatically learn and adjust control strategies for energy efficiency without requiring expert knowledge, as the feedback-driven learning process replaces manual optimization.
3Use of energy by moving object
If manual monitoring and control of peripheral machines is performed, then energy consumption can be managed, but manual labor is required and updates are frequent
Solution Approach 1:
The control system automatically monitors and manages energy consumption of peripheral machines without manual intervention. The system self-regulates control parameters based on learned patterns from sensor data, thereby managing energy consumption while eliminating the need for manual monitoring and reducing the frequency of expert updates.
Solution Approach 2:
The system implements continuous automated monitoring and control of peripheral machines, replacing intermittent manual monitoring. The control model operates continuously to optimize energy consumption, and the system automatically adapts to changes without requiring frequent manual updates, thereby improving productivity while maintaining energy management.
Data Source
AI summary
A method of applying feedback control on peripheral units supplying energy to manufacturing units in a manufacturing facility uses a trained model, and includes:mapping supply relations between peripheral units and manufacturing units into a schema;establishing communications with sensors monitoring peripheral unit metrics indicative of energy transfer from the peripheral units to the manufacturing units;training the model based on the schema and training data gathered by communication with the sensors during a training period, the trained model predicting energy usage by the peripheral machines with a specified degree of accuracy; andduring a control period following the training period, gathering further data from the sensors and minimizing energy usage by the peripheral units while supplying the total energy demanded by the manufacturing units by controlling at least one of the peripheral units based on an outcome of inputting the further data into the trained model.