Device Lifetime Prediction Using Aging Models and Bayesian Updates
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Solution Overview
Problem
Existing device monitoring methods are not sufficiently accurate in predicting failures, leading to potential catastrophic consequences and unnecessary maintenance costs.
Innovation Solution
A method involving an aging model with End of Life (EOL) boundary conditions and probability density functions is used to calculate the health prediction and remaining useful life of devices, incorporating measurements and Bayesian inference updates to generate reliable health signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical predictions with predetermined maintenance intervals are used, then maintenance costs are controlled, but prediction accuracy is insufficient leading to unexpected failures
Solution Approach 1:
The patent transforms the monitoring approach from fixed time-interval checks to continuous parameter-based monitoring using probability density functions. The system dynamically adjusts maintenance predictions based on real-time parameter deviations from the PDF, enabling accurate failure prediction without requiring complex hardware modifications.
Solution Approach 2:
The patent replaces traditional mechanical sensor-based monitoring systems with a mathematical modeling approach using probability density functions and Bayesian inference. This substitution reduces hardware complexity while improving prediction accuracy by using statistical models to capture device degradation patterns.
2Measurement precision
If physical sensors are used for on-line monitoring, then real-time device observation is achieved, but measurement precision is insufficient for accurate failure prediction
Solution Approach 1:
The patent establishes probability density functions during the device's normal operation phase (before failure) to model the statistical behavior of aging variables. This preliminary characterization enables accurate failure prediction without requiring measurements at or near the failure point, optimizing maintenance timing while maintaining high precision.
Solution Approach 2:
The system continuously updates the probability density function using Bayesian inference based on real-time measurements of aging variables. This feedback mechanism refines the failure prediction accuracy over time, allowing precise determination of optimal maintenance timing while reducing unnecessary early maintenance interventions.
3Reliability
If regular predetermined maintenance is performed, then device reliability is maintained, but unnecessary maintenance costs are incurred
Solution Approach 1:
The patent transitions from static predetermined maintenance schedules to dynamic condition-based maintenance predictions. The system continuously updates the probability density function and calculates failure probabilities based on real-time device state, enabling maintenance to be performed only when actually needed rather than following fixed intervals, thus reducing unnecessary maintenance costs while maintaining reliability.
Solution Approach 2:
By establishing probability density functions during normal operation, the system predicts failure points in advance with high accuracy. This preliminary prediction capability allows maintenance to be scheduled optimally - just before actual failure occurs - avoiding both premature maintenance and unexpected failures, thereby reducing overall maintenance costs.
Data Source
AI summary
A method for lifetime prediction and monitoring of a device, and a corresponding system are provided. The method comprises calculating a probability density function over time for an aging variable based on solving equation(s) from an aging model with an End of Life (EOL) boundary condition, wherein the boundary condition includes a first boundary condition and a second boundary condition, wherein the first boundary condition is a no-flux boundary condition and the second boundary condition is an absorbing or partly absorbing boundary condition, measuring an condition related observable of the device; obtaining first data representing measurement of the observable, calculating a likelihood for the aging variable from the first data, updating the calculated probability density function of the aging variable based on the likelihood, and generating a signal indicating a health prediction of the device based on the probability density function, the aging model and the EOL boundary condition.