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Related Experiment Video

Updated: May 10, 2025

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Remaining Useful Life (RUL) Prediction Based on the Bivariant Two-Phase Nonlinear Wiener Degradation Process.

Lijun Sun¹, Yuying Liang¹, Zaizai Yan¹

¹College of Science, Inner Mongolia University of Technology, Hohhot 010051, China.

Entropy (Basel, Switzerland)

|April 26, 2025

Summary

This summary is machine-generated.

This study introduces a new framework for predicting product lifespans in complex systems. It uses a multi-phase Wiener process model to improve reliability analysis and remaining useful life (RUL) prediction for enhanced engineering practices.

Keywords:

RUL prediction Schwarz information criterion turbine engine two-phase Wiener process

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Area of Science:

Engineering
Statistics
Reliability Theory

Background:

Aerospace and related sectors see products with enhanced reliability and lifespans.
Traditional models struggle with complex, nonlinear degradation in modern products.
Accurate prediction of product lifespans during service is increasingly critical.

Purpose of the Study:

Propose a framework for multiperformance, multi-phase Wiener process modeling.
Enhance reliability analysis for products with complex degradation processes.
Develop accurate methods for predicting remaining useful life (RUL).

Main Methods:

Introduced a two-phase nonlinear Wiener degradation model.
Identified change points using the Schwarz Information Criterion (SIC).
Derived RUL using the first hitting time (FHT) concept and copula functions for performance index correlation.

Main Results:

Developed an analytical formula for RUL considering stochastic degradation at change points.
Utilized Akaike Information Criterion (AIC) and copula functions for parameter estimation.
Presented a two-step method for estimating uncertain model parameters.

Conclusions:

The proposed framework advances reliability theory and engineering practices.
The multi-phase Wiener process model effectively handles complex degradation.
Case study validation demonstrates the framework's practical applicability in reliability engineering.