Optimal Diagnosis Strategy via Functional Extremum Transformation and Fault Elimination Neural Network: A Minimum Diagnosis-Cost Approach for Complex Systems

For complex systems with complex structures and limited fault data, expert knowledge-based methods are often preferred. The multi-signal flow graph model is a representative qualitative approach, but generating a minimum-cost diagnostic strategy under this model becomes increasingly difficult as system scale grows, leading to excessive diagnostic cost or unacceptable computation time. To address this issue, this paper reformulates the minimum-cost diagnostic strategy generation problem (MDP) as a functional extremum value problem (FEVP) through a multipartite graph model, which decomposes the recursive strategy-generation process into deterministic combination and nondeterministic elimination. This reformulation is motivated by the observation that only a small fraction of generated fault subsets contributes to the optimal strategy. Based on this insight, a fault elimination neural network with an intelligent algorithm (ENI) structure is proposed to learn an elimination function that retains promising subsets while discarding redundant ones. The neural network scores candidate fault subsets from fixed-dimensional statistical features, while the intelligent algorithm optimizes the non-differentiable objective. Simulation and real-case results show that the proposed method reduces diagnostic cost by more than 30% while maintaining acceptable computation time, with greater advantages on larger-scale systems.