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.

Temperature sensor systems are critical for nuclear power plant (NPP) condition monitoring, whose reliability underpins unit safety and stability. Fault localization and diagnosis are essential to sustain their stable service. Conventional Principal Component Analysis (PCA) and Graph Neural Network (GNN) methods suffer clear drawbacks: PCA is vulnerable to noise and cannot classify fault types accurately, while GNNs struggle to quantify correlations among temperature data. This paper fuses PCA’s anomaly representation capability and GNN’s structural feature extraction capacity to propose an Abnormal Feature-GCN method for joint fault localization and diagnosis. First, an Adaptive PCA (APCA) model fed with multi-dimensional sensor features computes abnormal features. These features are then transformed into edge weights to construct a weighted graph. A dual-branch GCN is finally trained via a joint loss function for parallel multi-task learning to simultaneously locate faulty sensors and identify fault types. Validated on a nuclear primary circuit temperature sensor system under constant-, rising-, and falling-temperature working conditions, the proposed method realizes accurate fault localization and classification. The mean overall accuracy of the proposed method surpasses mainstream baselines by 2.23%, 1.62%, and 1.89% for the three typical working conditions, respectively.

The large-scale integration of renewable energy loads has made distribution network waveforms strongly non-stationary. In addition to conventional harmonics and voltage deviations, power quality events with highly random triggering and extremely short durations occur frequently, posing a serious threat to precision manufacturing and high-reliability power supply scenarios. Existing detection methods, however, are either sensitive to window length and threshold settings or depend on offline labeled samples, and are therefore difficult to adapt to online anomaly discovery under continuous sampling. To address these problems, this paper proposes an online detection method based on fixed feature functionals and an adaptive normal reference. In this method, the feature centers, scales, and reliability weights are estimated only from normal windows in the training partition, and the alarm threshold is calibrated on a labeled validation partition, forming a site-adaptive online detector fitted on normal data and calibrated on a validation set. During operation, each new window is first scored against the normal reference at the previous instant, and the reference is updated only when the score falls below the update gate threshold. Experiments show that the proposed method achieves an event-level precision of 91.93%, a recall of 91.11%, and an F1 of 91.52%, providing higher detection sensitivity than comparable algorithms.