Component Lifetime Prediction Using Aging Pattern Anchor Values
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Solution Overview
Problem
Existing methods for predicting the remaining lifetime of components are inefficient, requiring extensive historical data and user input for pattern recognition, and fail to adapt to changing conditions or component replacements.
Innovation Solution
A method that senses system parameters, fits an aging pattern to the data, and determines a remaining lifetime parameter by retaining anchor values, using a numerical method that automatically selects and adapts to different aging patterns, detects component changes, and resets when necessary, without requiring extensive historical data or user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive historical data is retained for pattern recognition, then prediction accuracy is improved, but storage requirements and data processing complexity increase
Solution Approach 1:
The patent extracts only the essential information from historical data by identifying and retaining anchor values that represent critical states (initial, intermediate, final). Instead of storing all historical data points, the system extracts key characteristic values that capture the essential aging pattern, thereby reducing storage requirements while maintaining prediction accuracy.
Solution Approach 2:
The patent transforms the raw historical data into a simplified parameter representation by identifying anchor values at specific time points. This parameter transformation converts extensive continuous data into discrete key parameters (anchor values) that define the aging pattern, reducing data volume while preserving the essential information needed for accurate prediction.
2Adaptability or versatility
If the method requires user input for pattern recognition, then prediction can be customized, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically identifying anchor values and determining aging patterns without requiring user input. The algorithm autonomously processes the sensed data, selects relevant anchor points, and generates predictions, eliminating the need for user intervention while maintaining adaptability to different aging scenarios.
Solution Approach 2:
The system uses feedback mechanisms to automatically adjust and refine the aging pattern recognition. By continuously monitoring the sensed data and comparing it against the identified anchor values, the system self-corrects and adapts to actual component behavior, eliminating the need for manual pattern customization while maintaining high adaptability.
3Duration of action of moving object
If the method cannot detect component changes, then continuous monitoring is maintained, but reliability deteriorates due to inaccurate predictions after component replacement
Solution Approach 1:
The system employs feedback mechanisms to detect component changes by monitoring deviations from the established aging pattern. When the sensed data diverges significantly from the predicted trajectory based on anchor values, the system identifies a component replacement event and automatically resets the aging pattern recognition, thereby maintaining reliability after component replacement while preserving continuous monitoring capability.
Data Source
AI summary
A method for predicting a remaining lifetime parameter of a component installed in a system in particular of an engine component and/or a filter. The method includes, in one example, repeatedly sensing at least one parameter of the system to obtain a history of data values, fitting an aging pattern to the data values, and determining a remaining lifetime parameter of the component from the aging pattern, wherein at least some data values are erased with time such that the fitting is based on a subset of the data values determined since an initialization of the algorithm, wherein data values from an initial phase are not erased but retained as anchor values for the fitting throughout the lifetime determination of the component.