Explainable Change-Point Detection and Adaptive Anomaly Identification in Nonstationary Industrial Sensor Streams

Industrial sensor streams evolve under changing operating regimes, uncertain measurement quality, delayed supervision, and equipment degradation. This conceptual study develops an interpretable architecture that distinguishes detection of temporal regime changes from identification of anomalous episodes and verification of their physical causes. Classical sequential methods, online change-point inference, adaptive regime-conditioned baselines, multivariate corroboration, and feature-attribution diagnostics are examined as complementary sources of evidence. Particular attention is given to data provenance, alarm consolidation, uncertain event boundaries, contamination of adaptive baselines, and explanations that distinguish predictive influence from causal responsibility. A structured evidence matrix clarifies what different diagnostic signals can support and how their limitations should be assessed. The proposed evaluation principles emphasize chronological testing, event-level operational burden, robustness to acquisition failures, human review, and reproducible model updates. No unreported field experiments or quantitative performance gains are claimed. The resulting framework outlines conditions under which a sensor-stream alert can remain traceable, technically intelligible, and contestable as the monitored process changes.