Equipment RUL Prediction Using Dynamic Failure Thresholds
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Solution Overview
Problem
Existing methods for estimating the remaining useful life (RUL) of equipment components are limited by their reliance on static failure thresholds, which do not account for unique degradation characteristics and environmental conditions, making them inaccurate for real-life scenarios where prior knowledge is unavailable.
Innovation Solution
A method that calculates a dynamic failure threshold based on degradation characteristics, using sensor data to extract features, apply principal component analysis for health indicators, and fit an exponential curve to predict future degradation, with optional particle swarm optimization for parameter estimation, allowing the threshold to adapt to each component's unique degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed static failure threshold is used based on prior knowledge, then the RUL estimation method is simple to implement, but the prediction accuracy deteriorates when prior knowledge is unavailable or when environmental conditions vary
Solution Approach 1:
The patent transforms the static failure threshold into a dynamic one that adapts to each component's unique degradation characteristics. The threshold evolves over time based on the component's actual degradation trajectory, environmental conditions, and operational parameters, allowing the system to maintain high prediction accuracy without relying on pre-established prior knowledge
Solution Approach 2:
The patent changes the parameters used to define the failure threshold from fixed values to time-varying parameters that are continuously updated based on monitored degradation data. This allows the threshold to adapt to changing environmental conditions and component-specific degradation patterns, resolving the contradiction between implementation simplicity and prediction accuracy
2Measurement precision
If similarity-based approaches are used with run-to-failure data, then the RUL threshold can be determined based on component similarity, but the method becomes infeasible when failure characteristics are not available
Solution Approach 1:
The patent enables the system to determine its own failure threshold without external prior knowledge or reference to other components' failure data. Each component serves itself by learning its unique degradation pattern from its own operational data, making the method universally applicable to any component regardless of whether failure characteristics are available
Solution Approach 2:
The patent performs preliminary adaptation by continuously learning and adjusting the failure threshold based on early degradation signals. This preliminary action allows the system to establish component-specific thresholds even when no prior failure data exists, enabling deployment in real-life scenarios where failure characteristics are unavailable
3Measurement precision
If deep learning techniques are deployed to predict RUL, then the prediction capability is enhanced, but the system complexity and computational requirements increase
Solution Approach 1:
The patent extracts and focuses on the critical function of failure threshold determination, separating it from complex deep learning architectures. By dedicating specific computational efforts to threshold adaptation based on degradation characteristics, the system achieves enhanced prediction capability while maintaining manageable complexity through targeted rather than comprehensive complexity
Data Source
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AI summary
A method and a system to predict remaining useful life of an equipment is disclosed wherein a processor (1) is adapted to receive signals from the sensors (2) of the equipment (3); extract features from the sensor data; obtain a health indicator from the extracted features by principal component analysis; determine a critical time beyond which the degradation initiates in the equipment using pautas criteria; predict a future degradation curve; and determine the dynamic failure threshold based on degradation characteristics of said equipment. The dynamic failure threshold is determined in real time based on the degradation parameters unique to said equipment.