State reliability analysis methods for nonlinear systems integrating adaptive entropy weighted grey relation and...

Area of Science:

• Engineering
• Systems Analysis
• Reliability Engineering

Background:

• Uncertain environmental conditions cause nonlinear systems to experience information conflicts, ambiguity, and loss, hindering accurate abnormal state prediction.
• System failures under uncertainty lead to significant negative impacts, necessitating improved predictive and assessment methodologies.

Purpose of the Study:

• To develop an advanced analysis model for nonlinear systems operating under uncertain conditions.
• To enhance the accuracy of predicting abnormal system states and assessing system reliability.

Main Methods:

• Incorporation of factor weight adaptive adjustment within the analysis model.
• Integration of Dempster-Shafer's theory (D-S theory) algorithms for evaluation and quantification of uncertainty and correlation factors.

Main Results:

• The proposed model achieved a system state identification accuracy of 97% in a wind turbine case study.
• System reliability probability was assessed at 65%, showing a 5.22% improvement over traditional methods.
• Overall accuracy improvement of 4.5% was observed compared to existing algorithms.

Conclusions:

• The developed model effectively reduces the impact of information uncertainty on analysis accuracy.
• The approach enhances the precision of system reliability probability assessment, improving decision-making.
• The algorithm demonstrates superior performance in aligning with actual system state probability distributions.