Accelerated Degradation Data for Component Phase Identification
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Solution Overview
Problem
Industrial settings face challenges in precisely identifying the degradation of physical components due to varying environmental conditions and operational wear, leading to incorrect replacement or maintenance decisions.
Innovation Solution
A system that applies an accelerated degradation process to physical components, obtaining measurement data on visual characteristics at multiple phases of degradation to train a neural network for accurate identification of degradation phases in other components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accelerated degradation process is applied to physical components, then training data quality and quantity improve, but time and resource consumption increase
Solution Approach 1:
The patent applies accelerated degradation by changing environmental parameters (temperature, humidity, stress loads) to intensify the degradation process. This allows collecting multiple phases of degradation data in compressed time frames, resolving the contradiction between data quality and time consumption by manipulating physical parameters to speed up degradation while maintaining measurement accuracy
Solution Approach 2:
The patent performs preliminary accelerated degradation testing to generate training data before deploying the neural network for actual degradation identification. By pre-collecting comprehensive degradation phase data under controlled accelerated conditions, the system prepares high-quality training datasets that enable accurate real-time degradation assessment without requiring time-consuming data collection during operational use
2Reliability
If measurement data is collected at multiple phases of degradation, then training data comprehensiveness improves, but measurement and data processing complexity increase
Solution Approach 1:
The patent segments the degradation process into distinct phases (early, intermediate, advanced degradation) and collects measurement data at each phase separately. This segmentation allows the measurement system to focus on capturing phase-specific characteristics, reducing overall complexity while ensuring comprehensive coverage of the entire degradation trajectory for reliable neural network training
Solution Approach 2:
The patent utilizes changes in physical and chemical parameters (visual characteristics, material properties, performance metrics) at different degradation phases to create distinct measurement signatures. By monitoring how these parameters evolve through degradation stages, the system achieves comprehensive training data collection with manageable measurement complexity, as each phase presents identifiable parameter patterns
3Productivity
If accelerated degradation is applied, then data collection speed improves, but component damage and loss increase
Solution Approach 1:
The patent employs disposable or sacrificial test specimens for accelerated degradation testing. These specimens are intentionally subjected to severe accelerated conditions to generate training data, accepting their complete degradation or failure as acceptable loss. This approach enables high-speed data collection without compromising operational components, as the degraded specimens are replaced rather than reused
Solution Approach 2:
The patent creates copies or replicas of physical components for accelerated degradation testing. These duplicate specimens serve as training data sources, undergoing accelerated degradation while the original operational components remain intact. This copying strategy allows aggressive data collection methods on replicas without causing loss to the actual functional components
Data Source
AI summary
Technologies for producing training data for identifying degradation of physical components include a system. The system includes circuitry configured to apply an accelerated degradation process to a physical component of an industrial plant. Additionally, the circuitry of the system is configured to obtain measurement data indicative of visual characteristics of the physical component at each of multiple phases of degradation, wherein the measurement data is usable to train a neural network to identify a phase of degradation of another physical component.