Physics-Based Particle Filter for Cable RUL Prediction
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Solution Overview
Problem
Current fault diagnosis and prognosis methods for load-bearing cables, particularly those made of conductive materials, face challenges in accurately predicting remaining useful life due to high computational resource requirements in model-based approaches and the need for large datasets in data-driven methods, while also requiring efficient monitoring of fatigue damage under cyclic loading conditions.
Innovation Solution
A system that employs physics-based modeling, specifically a grey-box model combining analytical and semi-analytical components, to estimate the useful life of load-bearing structures by measuring conductive properties like electrical resistance and using Bayesian inference-based filtering algorithms, such as particle filtering, to infer damage growth rates and total useful life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based diagnostic techniques are used for fault diagnosis, then prediction accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent transforms the complex model-based diagnostic approach into a simplified parameter-based method. Instead of performing comprehensive model-based analysis, the system monitors specific electrical resistance parameters and compares them against pre-established thresholds. This parameter change approach maintains prediction accuracy by focusing on critical indicators while dramatically reducing computational resource requirements.
2Productivity
If data-driven approaches are used for fault diagnosis, then computational efficiency is improved, but data requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-establishing resistance thresholds and diagnostic criteria before actual fault diagnosis occurs. During operation, the system only needs to measure current resistance values and compare them against these pre-computed thresholds, eliminating the need for large historical datasets and complex real-time data processing. This approach achieves computational efficiency while minimizing data requirements.
3Reliability
If comprehensive model-based diagnosis is implemented, then fault detection capability is improved, but system complexity increases
Solution Approach 1:
The patent extracts the essential diagnostic function from complex model-based systems by isolating and monitoring only the critical electrical resistance parameter. Instead of implementing comprehensive models that analyze multiple parameters and system states, the invention extracts and monitors the specific resistance indicator that correlates with cable degradation, thereby maintaining fault detection capability while significantly reducing system complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate prediction of remaining useful life by linking mechanical and conductive properties, allowing for efficient monitoring of structural damage and fatigue accumulation, thereby reducing computational resources and data requirements, and providing reliable predictions even under conditions with limited data availability.
Implementation Method 1
performs a dynamic measurement to obtain at least one conductive property of the load-bearing structure as a function of fatigue cycles... the at least conductive property comprises one or more of: an electrical resistance
Data Source
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AI summary
One embodiment can provide a system for estimating a useful life of a load-bearing structure at least partly made of a conductive material. During operation, the system establishes a physics-based damage model for the load-bearing structure, performs a dynamic measurement to obtain at least one conductive property of the load-bearing structure as a function of fatigue cycles, estimates parameters of the physics-based damage model based on the measured conductive property, and estimates the useful life of the load-bearing structure based on the estimated parameters of the physics-based damage model.