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Updated: Jul 5, 2025

The Effect of Charging and Discharging Lithium Iron Phosphate-graphite Cells at Different Temperatures on Degradation

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Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability.

Xuemiao Cui¹, Jiping Lu¹, Yafeng Han¹

¹School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

This study introduces a new model for predicting the remaining useful life (RUL) of degrading devices by accounting for temporal, unit-to-unit, and measurement variabilities. Incorporating all three variabilities simultaneously is crucial for accurate RUL prediction.

Keywords:

degradation modeling nonlinear Wiener process prognostics remaining useful life uncertainty variability

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Area of Science:

Reliability Engineering
Degradation Modeling
Predictive Maintenance

Background:

Remaining Useful Life (RUL) estimation is critical for operational safety.
Existing models partially address temporal, unit-to-unit, and measurement variabilities.
A comprehensive approach is needed for two-phase nonlinear degrading systems.

Purpose of the Study:

To develop a two-phase nonlinear degradation model incorporating three sources of variability.
To derive an analytical solution for RUL estimation considering these variabilities.
To validate the proposed model with numerical and real-world data.

Main Methods:

Nonlinear Wiener process for modeling degradation.
First passage time (FPT) for RUL solution derivation.
Maximum Likelihood Estimation (MLE) for offline parameter estimation.
Bayesian rule with Kalman Filtering (KF) for online model updating.

Main Results:

An approximate analytical solution for RUL with three-source variability was derived.
Offline and online methods were successfully applied for parameter estimation and model updating.
Validation confirmed the necessity of considering all three variabilities simultaneously.

Conclusions:

The proposed model effectively estimates RUL for two-phase nonlinear degrading systems.
Simultaneous consideration of temporal, unit-to-unit, and measurement variabilities is essential for accurate RUL prediction.
This approach enhances the reliability and safety of degrading systems.