Abstract: Modern industrial cyber-physical systems (CPS) rely on dense sensor networks whose telemetry is routinely corrupted by measurement noise, transmission jitter, sensor drift, and occasionally adversarial perturbation. Such corruption degrades not only the raw accuracy of learning-based fault detectors but also the broader performability of the monitoring system that it’s intended to deliver correct, timely, and available detections under degraded operating conditions. This paper introduces PerforNet, a noise-resilient, attention-guided contrastive learning framework that jointly optimizes detection accuracy and quantified system performability. PerforNet combines (i) a dual-branch channel-temporal attention encoder that learns to down-weight historically unreliable sensor channels rather than relying solely on instantaneous signal salience, and (ii) a performability-weighted contrastive objective that discounts the contribution of negative pairs whose apparent dissimilarity is attributable to noise rather than true semantic (fault) difference. We further propose a Composite Performability Index (CPI) that unifies detection F1-score, false-alarm rate, and operational availability into a single interpretable benchmarking score, bridging machine-learning evaluation with classical reliability engineering. Experiments across three CPS benchmarks under controlled sensor-noise injection show that the proposed framework improves detection F1-score over strong contrastive and reconstruction-based baselines while substantially reducing false-alarm frequency, with ablations attributing the majority of the robustness gain to the performability-weighting term rather than the attention module alone. The framework offers a practical and reproducible route toward performability-aware design of intelligent monitoring systems for safety-critical infrastructure.

Key words: performability, reliability engineering, contrastive learning, attention mechanism, fault detection, cyber-physical systems, noise robustness, availability