Assessment of fault diagnosis methods in nonlinear circuits identifies challenges, highlighting the role of artificial intelligence and testability.
The paper focuses on fault diagnosis in nonlinear analog circuits, a topic that has been explored in the literature for over 50 years but remains unresolved due to its complex nature. It reviews various aspects of nonlinear circuit diagnosis, including methods for assessing testability, selecting diagnostic tests, and conducting diagnostic processes through simulation before and after test methods. The paper also discusses the use of artificial intelligence tools in this context. Given the specific characteristics of nonlinear circuits used as DC-DC converters and the wealth of the existing literature—including review papers on the subject—this issue is addressed only briefly. The main aim of the paper is to identify research challenges in fault diagnosis for a general class of nonlinear circuits. To illustrate and discuss these challenges, the paper provides examples, including ambiguity in diagnostic equation solutions, multiple equilibrium points, and the effects of self-heating. These examples can serve as simple benchmarks for new proposals to advance comprehensive diagnosis methods for nonlinear circuits.
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Stanisław Hałgas (2025) studied this question.
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