Prognostic Vector Prediction for Complex System Availability
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Solution Overview
Problem
Current health management systems for complex vehicles and systems can determine the current status of key functions based on lower-level components but fail to predict future availability, lacking a mechanism to compute and provide both current and predicted values for functional availability.
Innovation Solution
The method involves converting binary parameters of a complex system into prognostic vectors and using a fuzzy converter to transform binary input expressions into fuzzy output expressions operable on these vectors, enabling the prediction of functional availability through prognostic vectors and fuzzy prediction evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary values and complex logic expressions are used to determine system status, then the current status can be determined accurately, but the ability to predict future availability is lost
Solution Approach 1:
The patent transforms binary parameters (0 or 1) into continuous prognostic vectors that can represent degradation states and predict future availability. This parameter transformation enables the system to maintain accurate current status determination while adding predictive capability, resolving the contradiction between measurement precision and reliability forecasting.
Solution Approach 2:
The patent introduces prognostic vectors as an intermediary between binary component status and system-level availability prediction. These vectors serve as a bridge that preserves the accuracy of current status determination while enabling future availability forecasting, thus resolving the contradiction between precise measurement and predictive reliability.
2Loss of information
If health management systems monitor multiple aspects of the vehicle, then more comprehensive system status is achieved, but the complexity of determining key function status increases
Solution Approach 1:
The patent segments the complex logic expression evaluation into two distinct phases: (1) converting binary parameters to prognostic vectors for individual components, and (2) evaluating the converted expression with prognostic vectors to determine system availability. This segmentation reduces the complexity of handling multiple monitored aspects while maintaining comprehensive system status information.
Solution Approach 2:
By changing the parameter representation from binary to continuous prognostic vectors, the patent simplifies the evaluation process for systems monitoring multiple aspects. The continuous parameters allow for more straightforward mathematical operations and predictions, reducing the overall complexity despite the increased comprehensiveness of system monitoring.
Data Source
AI summary
A method, system, and computer program product for predicting the functional availability of a complex system is provided. Parameters of the complex system are converted from a plurality of binary values to at least one prognostic vector. At least a portion of a binary input expression is converted into an equivalent fuzzy output expression, the fuzzy output expression operable on the at least one prognostic vector.