Compressed-Air Component Control Using Local Predictive Models
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Solution Overview
Problem
Existing control systems for compressed-air generation, processing, storage, and distribution rely heavily on central control devices, which can be overwhelmed by data flow and lack precision in monitoring and diagnosing components, leading to inefficiencies and potential malfunctions.
Innovation Solution
An electronic control device that utilizes component-related models to evaluate and control operational data, states, and behaviors, allowing for more accurate monitoring and diagnosis by referencing structural and behavioral information, and using historical and current data from sensors to optimize operations and predict future states without the need for extensive sensor networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a central control device is used to control and monitor multiple compressed-air components, then system-wide control capability is improved, but data processing burden and control precision for individual components deteriorate
Solution Approach 1:
The control system is segmented into a central control device for system-wide coordination and distributed electronic control devices for individual component control. Each component (compressor, dryer, filter, storage vessel) has its own control device that independently processes sensor data and executes control actions, while the central device manages overall system coordination and data aggregation.
Solution Approach 2:
Electronic control devices serve as intermediaries between sensor networks and the central control device. These intermediary controllers pre-process sensor data, perform local diagnostics using component-specific models, and filter information before transmitting to the central device, reducing data processing burden and improving response time.
2Measurement precision
If extensive sensor networks are deployed to monitor all components, then measurement coverage is improved, but system complexity and cost increase
Solution Approach 1:
Component-specific models serve as virtual copies of physical components, replicating their behavior and characteristics in software. These models use mathematical relationships and historical data to simulate component operation, allowing the system to infer unmeasured parameters and predict failures without requiring additional physical sensors.
Solution Approach 2:
Each component's control device performs self-diagnosis and self-monitoring using locally available sensor data and component-specific models. The system automatically detects anomalies, diagnoses faults, and triggers maintenance alerts without requiring centralized analysis of every data point, reducing the need for extensive sensor networks.
3Reliability
If historical data is extensively collected and analyzed, then diagnostic accuracy is improved, but data processing time and computational burden increase
Solution Approach 1:
Component-specific models are pre-configured with manufacturer specifications, design parameters, and expected operational characteristics before deployment. Historical data patterns and failure modes are pre-analyzed and encoded into the models, enabling rapid real-time diagnosis without requiring extensive computational analysis during operation.
Solution Approach 2:
The system implements localized data processing at each component's control device, which maintains and analyzes historical data specific to that component using its dedicated model. This distributed approach allows parallel processing of multiple component histories simultaneously, reducing overall system processing time while maintaining diagnostic accuracy.
Data Source
AI summary
An electronic control device for a component of compressed-air generation, compressed-air processing, compressed-air storage, and/or compressed-air distribution falls back upon one or more models, which, as component-related models, contain information relevant to the structure, or the behavior of the component, to determine, simulate, or evaluate operation-relevant data and performs, as an evaluation purpose, either—open-loop control, closed-loop control, diagnosis, and/or monitoring of the component or—a determination, provision, prediction, or optimization of operating data, operating states, operating modes, operating behaviors, and/or operating effects on the basis of the models in a concrete evaluation routine. Current or historical structure information operating data, operating states, and/or measurements/sensor values of the component at least partially available in the electronic control device are used as initial values.