Model-Based Decentralized Control for Compressed Air System Components
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Solution Overview
Problem
Existing methods for controlling and monitoring compressed air generation, treatment, and distribution systems rely heavily on central control units, which can be overwhelmed by data demands and lack precision in diagnosis and monitoring.
Innovation Solution
A method and electronic control device that utilize component-specific models to evaluate operational data, allowing for precise control, regulation, diagnosis, and monitoring by using models that adapt to the component's structure and behavior, enabling self-learning and comparison with actual data to identify malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a central control unit controls and monitors a large number of components for compressed air generation and/or treatment, then comprehensive system control is achieved, but the control unit becomes overburdened and data flow requirements increase significantly
Solution Approach 1:
The patent divides the control system into decentralized control units, each responsible for specific components (compressors, dryers, filters). Each control unit independently manages its assigned components using local models and sensor data, eliminating the need for a single overloaded central control unit while maintaining comprehensive system control.
Solution Approach 2:
The patent introduces model-based evaluation routines as intermediaries between sensors and control decisions. These models process sensor data locally at each control unit, transforming raw data into meaningful operational insights and control actions without requiring all data to flow through a central unit.
2Measurement precision
If component-related models are used to determine, replicate or evaluate operationally relevant data, then control and diagnosis precision is improved, but device complexity increases due to model implementation
Solution Approach 1:
The patent creates virtual copies of component behavior through mathematical models that replicate the operational characteristics of physical components. These model copies enable precise evaluation of operational data, prediction of component behavior, and diagnosis of malfunctions without adding physical complexity to the actual hardware.
Solution Approach 2:
The patent transforms complex control problems into manageable parameter comparisons by evaluating model predictions against actual sensor measurements. This approach converts complex system behavior analysis into straightforward parameter matching and deviation detection, simplifying the control logic while maintaining precision.
3Measurement precision
If alternative evaluations with different malfunction configurations are performed for diagnosis, then diagnosis accuracy is improved, but evaluation time and computational load increase
Solution Approach 1:
The patent pre-configures multiple malfunction models representing different failure modes before actual diagnosis is needed. These models are prepared in advance with their characteristic behavior patterns, enabling rapid comparison against actual sensor data during diagnosis without requiring time-consuming analysis of failure mechanisms during the diagnostic process.
Solution Approach 2:
The patent evaluates multiple malfunction scenarios simultaneously by comparing sensor data against several pre-configured models in parallel. This excessive evaluation approach ensures that the correct malfunction is identified even if some models are initially considered, while the model that best matches the actual behavior emerges as the diagnosis.
Data Source
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AI summary
Electronic control device for a component of compressed air generation, compressed air treatment, compressed air storage and/or compressed air distribution, wherein the electronic control device (11) uses one or more models, which contain relevant information as component-related models for the structure or behavior of the component (12), to determine, replicate or evaluate operationally relevant data, and, based on the models, performs in a specific evaluation routine either - control, regulation, diagnosis and/or monitoring of the component or - determination, provision, prediction or optimization of operational data, operating states, operating modes, operational behavior and/or operational effects, and wherein, at least partially available in the electronic control device, current or historical structural information, operational data,Operating states and/or measurement/sensor values of the component are used.