Storage reliability prediction of electromechanical components based on virtual manufacturing and testing

Area of Science:

• Reliability Engineering
• Materials Science
• Manufacturing Processes

Background:

• Electromechanical components (EMCs) are critical in industrial and military systems, but their storage reliability is hard to predict during design.
• Limited failure rate data, testing time, and budget hinder accurate prediction of EMC storage reliability.
• The storage reliability of EMCs directly impacts the overall system reliability.

Purpose of the Study:

• To propose a virtual manufacturing and testing method for simulating the storage degradation process of batch EMCs.
• To enhance the prediction of storage reliability for EMCs, addressing limitations in traditional methods.
• To improve the reliability assessment of systems incorporating EMCs.

Main Methods:

• A virtual manufacturing and testing approach is developed to simulate the storage degradation of EMCs.
• The Wiener process is utilized to model the storage failure distribution function, incorporating quality screening and unit-to-unit variability.
• Testing data from related products are used as prior information to quantify diffusion coefficient distributions and model uncertainty.

Main Results:

• A storage failure distribution function for batch EMCs is obtained, considering manufacturing influences and variability.
• The uncertainty in the storage degradation process is quantified by modeling the diffusion coefficient distribution.
• A case study on an electromagnetic relay demonstrates the effectiveness of the proposed virtual approach.

Conclusions:

• The proposed virtual method effectively simulates EMC storage degradation and predicts reliability.
• Accounting for manufacturing processes and unit variability improves the accuracy of storage reliability predictions.
• This approach offers a viable solution for estimating EMC storage reliability under data and resource constraints.