Online Test Strategy Generation Based on Delayed-Feedback Value Learning

Design for testability configures test points and plans tests during product design to support fault detection and isolation in service. Existing test strategy generation (TSG) methods mainly address offline diagnosis. As demand for uninterrupted operation grows, downtime and economic losses become more significant, shifting maintenance toward online diagnosis. However, offline methods optimize complete diagnostic processes without directly accounting for isolation gains within a limited time. Their direct application can therefore yield a low fault isolation rate (FIR). To address this problem, this paper proposes an online TSG method based on delayed-feedback value learning (DFVL). First, an optimization problem is formulated using a multi-signal flow graph and a finite time constraint. Second, a DFVL framework is developed using value recursion and feasible subtree returns. It comprises Bellman-based features, balanced feasible completions, delayed updates with unweighted normalized residuals, historical-tree retention, model-derived MAP candidates, and bounded local search. Across 90 synthetic models and 12 budgets, mean FIR was 83.53%, compared with 77.96% for Mix1 and 73.24% for DIG. At normalized Time 36, device-model FIR was 75.90%, versus 53.15% for Mix1 and 61.86% for DIG. FIR denotes a model-based correct terminal MAP decision probability under single-fault, deterministic-response, and fixed-duration assumptions.