Anomaly Localization as a Defense Against Sensor Faults in Virtual Sensing

Learning-based virtual sensors inherit the vulnerability of the physical sensors they consume. When a transducer drifts, saturates, or sticks, the input distribution shifts and predictions degrade. We tackle this with a detect–isolate–accommodate pipeline, in which a fault detector flags and discards failing sensors before they reach the predictor. This shifts the burden from learning invariance across the full fault manifold to recognizing departures from normality, collapsing the predictor's necessary training augmentation to plain channel dropout. To make this pipeline work, what matters is not only when a channel misbehaves but which one does. Yet time-series anomaly detectors are built and benchmarked almost exclusively for the former. We test whether they can serve as channel-level isolators end-to-end on a benchmark for sensor-failure robustness in virtual sensing, spanning six domains, nine datasets and ten failure modes. Finding that current state-of-the-art detectors localize faulty channels poorly, we propose itx, an iTransformer adaptation with untied per-channel embeddings and projections, trained with cross-instance channel substitution to simulate plausible-but-wrong sensor readings. It substantially improves channel-level localization and, paired with a dropout-trained back-end, makes explicit fault isolation the best-ranked robustification in our evaluation.