Integrated diagnosis optimization design of the electronic equipment based on spatial mapping

Area of Science:

• Electrical Engineering
• Computer Science
• Reliability Engineering

Background:

• Integrated diagnosis in electronic equipment is complex due to intricate test and fault information.
• Existing methods optimize test or resource allocation separately, ignoring their interdependence.
• Designing for equipment reliability requires addressing these interconnected optimization challenges.

Purpose of the Study:

• To propose a design strategy for integrated diagnosis optimization using the spatial mapping principle.
• To quantitatively define the constraint relationship between test and resource optimization.
• To develop an optimization model for enhanced electronic equipment reliability.

Main Methods:

• A spatial mapping principle is employed to model the logical relationship between test, resource, and fault spaces.
• The grey wolf optimization algorithm is utilized to determine optimal test and resource configurations.
• The proposed algorithm's efficiency is validated using benchmark functions and an electronic equipment model.

Main Results:

• The optimized integrated diagnosis achieved 100% critical fault detection, 99.99% fault detection, and 98.99% fault isolation with a 0.2993% false alarm rate.
• Integrated diagnosis optimization reduced the number of tests by 88.9% and saved 89% of test costs.
• The grey wolf optimization algorithm outperformed others, reducing tests by 42%-55% and costs by 77.63%-83.91%.

Conclusions:

• The proposed spatial mapping strategy effectively optimizes integrated diagnosis for electronic equipment.
• This approach significantly enhances test efficiency and dramatically reduces testing costs.
• The grey wolf optimization algorithm provides a robust solution for complex integrated diagnosis problems.