Causal Parameter Diagnosis for Hidden Machine Operating States
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Solution Overview
Problem
Complex systems, such as lithographic apparatuses, face challenges in diagnosing unobserved operational parameters, which can lead to incorrect monitoring, analysis, and control, resulting in potential failures and design issues due to incomplete data interpretation.
Innovation Solution
A method is introduced to determine causal relationships between observable parameters, decompose them into information components, and identify negative synergistic information components to diagnose unobserved operational parameters, allowing for adjustments and validation of system settings and designs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to observe system parameters, then observable parameters can be measured, but unobserved operational parameters cannot be diagnosed accurately
Solution Approach 1:
The patent introduces an intermediary computational framework that uses observable parameters as mediators to infer unobserved parameters. By decomposing information from observable parameters into unique, redundant, and synergistic components, the system indirectly diagnoses unobserved operational parameters without direct measurement, thus resolving the contradiction between measurement capability and information completeness.
Solution Approach 2:
The patent replaces traditional direct mechanical/measurement-based monitoring with an information-theoretic computational approach. Instead of physically measuring unobserved parameters, the system uses information decomposition and causal relationship analysis to substitute direct measurement with indirect inference, achieving diagnosis of hidden factors through data processing rather than physical sensing.
2Reliability
If more parameters are monitored to improve system diagnosis, then system performance can be better understood, but system complexity increases
Solution Approach 1:
The patent extracts and separates the information content of parameters into distinct components (unique information, redundant information, synergistic information). By taking out only the essential diagnostic information needed to infer unobserved parameters rather than monitoring all possible parameters directly, the system improves diagnosis reliability while avoiding the complexity of comprehensive direct monitoring of all system parameters.
Solution Approach 2:
The patent creates a universal diagnostic framework that can handle multiple types of parameters (observable and unobserved) using a single information decomposition approach. This multi-functional system can diagnose various unobserved parameters through the same computational methodology, improving reliability across different diagnostic scenarios without proportionally increasing system complexity.
3Measurement precision
If causal relationships between parameters are analyzed to diagnose unobserved parameters, then diagnostic accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the complex task of diagnosing unobserved parameters into manageable information components: unique information from individual observable parameters, redundant information shared between parameters, and synergistic information from parameter combinations. This segmentation of information analysis reduces computational complexity by breaking down the causal relationship analysis into structured, separable components that can be processed systematically.
Data Source
AI summary
An apparatus and method of diagnosing an unobserved operational parameter of a machine or apparatus. The method including obtaining a plurality of causal relationships between pairs of parameters of the machine or apparatus, wherein each pair includes a cause parameter and an effect parameter. For at least some of the parameters, a decomposition of the parameters into a plurality of information components is determined, based on the determined causal relationships between the parameters. The decomposition includes a synergistic information component including information obtained from a combination of at least two causal relationships having the parameter as effect parameter. A parameter is determined to include a negative synergistic information component. Based on the existence of the negative synergistic information component, it is diagnosed that an unobserved operational parameter provides a cause for the parameter including the negative synergistic information component.