Diagnostic Algorithm Parameter Optimization via Objective Function Minimization
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Solution Overview
Problem
Current health management systems for complex electronic and mechanical systems, such as aircraft, require specialized personnel to optimize diagnostic algorithm parameters through a time-consuming trial-and-error process, which is prone to suboptimal selection.
Innovation Solution
A method and system that utilize sensed data sets with associated labels to optimize diagnostic algorithm parameters by minimizing a generic objective function value using an optimization routine, allowing for automated parameter selection without relying on specialized personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized personnel manually optimize diagnostic algorithm parameters through trial and error, then diagnostic accuracy may be improved, but time consumption increases significantly
Solution Approach 1:
The system performs self-optimization of diagnostic algorithm parameters through automated machine learning workflows. The optimization process is executed autonomously using sensed data sets and objective functions, eliminating the need for manual intervention by specialized personnel while maintaining diagnostic accuracy.
Solution Approach 2:
The manual trial-and-error process performed by specialized personnel is replaced with an automated computational optimization system. Machine learning algorithms automatically adjust parameters based on objective functions, substituting human mechanical processes with automated computational methods.
2Reliability
If specialized personnel are involved in parameter selection, then diagnostic algorithm performance may be optimized, but system complexity increases due to dependency on expert knowledge
Solution Approach 1:
The system autonomously optimizes diagnostic algorithm parameters without requiring specialized personnel. The self-service mechanism uses automated workflows that execute optimization routines based on objective functions, eliminating dependency on expert knowledge while maintaining system reliability.
Solution Approach 2:
The optimization system is designed to be universally applicable across different diagnostic algorithms and applications. The generic objective function and automated workflow can handle various parameter optimization tasks without requiring specialized configurations, reducing system complexity while maintaining performance.
3Ease of manufacture
If manual trial and error methods are used for parameter optimization, then parameter selection may be performed, but the process becomes prone to suboptimal selection
Solution Approach 1:
The optimization process incorporates feedback mechanisms through objective functions that evaluate parameter performance. The system iteratively adjusts parameters based on feedback from the objective function evaluations, ensuring optimal parameter selection rather than relying on manual trial and error that lacks systematic feedback.
Solution Approach 2:
The system performs preliminary automated optimization of parameters before deployment. By pre-optimizing parameters using sensed data sets and objective functions, the system eliminates the need for manual trial and error, ensuring optimal parameter selection from the outset rather than through prone-to-error manual processes.
Data Source
AI summary
A system and method are provided for optimizing parameters of a plurality of selected diagnostic and/or prognostic algorithms in a tunable diagnostic algorithm library. A plurality of sensed data sets having an actual diagnostic label associated therewith is supplied to each of the diagnostic algorithms. A value for each parameter of each of the algorithms that are to be optimized is supplied. A computed diagnostic label is generated for each of the sensed data sets using each of the selected algorithms, a fault model, and the values for each parameter, each of the computed diagnostic labels and each of the actual diagnostic labels are supplied to a generic objective function, to thereby calculate an objective function value, and the value of one or more of the parameters is varied using an optimization routine that repeats certain of these steps until the objective function value is minimized.