A Detailed Review on Fault Diagnosis of Electronic Systems Using Intelligent Techniques

Abstract

This work look at different smart ways to find problems in electronic things, which getting more important as electronic stuff get more complex and need to work better. We talk about five main ways: Rule-Based, Model-Based, Case-Based, Fuzzy Logic and Neural Networks, and Hybrid Approaches. Each way have good and bad points. Rule-Based use expert knowledge but hard to keep up. Model-Based try to copy how things work but often too slow for big systems. Case-Based learn from old problems but need lots of examples. Fuzzy Logic and Neural Networks good with unclear stuff but sometimes hard to understand. Hybrid Approaches mix these ways to get the best parts of each. We look at how these ways work, where they used, and what problems they have. We also talk about what might happen with these ways in the future. Smart ways to find problems help electronic things work better and cost less to fix. They used more and more in important areas like flying, health care, and big machines. These ways can look at lots of information fast and find problems quick, which really important for keeping things safe and working. The history of using smart ways to find problems in electronic things go back many years. It start with simple computer thinking in the 1980s and 1990s. Then it get better with new math ideas in the 2000s. Now, with big computer power and lots of data, machine learning getting really good at finding problems. As electronic things keep getting more complex, these smart ways to find problems will probably get even more important. The big goal is to make electronic things that can find and fix their own problems, so they work better and need less fixing by people.