From Sensor Streams to Bounded Machine Memory: Architecture, Negative Transfer Results, and Domain Specialists

Industrial and edge equipment produce continuous telemetry that cannot be kept in full on flash storage, while first-in-first-out buffers overwrite rare fault episodeswith routine data. We study μEngram, a bounded stateful memory that compresses short sensor windows into noise-whitened low-dimensional vectors, tracks operating regimes with a fixedbudget of semantic, candidate and episodic slots, quarantines unverified shifts, and pins raw waveform slices for rare episodes. We evaluate it and several encoder variants on a syntheticgenerator and ten public benchmarks: CMAPSS, MetroPT-2 and MetroPT-3, SKAB, SCANIA Component X, OS-Metrics, EngineAD, MFPT, CWRU and TSB-AD-U. On the synthetic stream the memory cutswrite operations by 86.2% over 10 seeds while retaining every injected event. Across the benchmarks, alarm behavior is governed primarily by threshold design, calibration coverage andalarm-counting rules rather than rank-ordering metrics: models reaching 1.0000 AUROC on CWRU still alarm throughout clean normal operations. Time-series foundation models and theirdistilled student models shifted alarm timing without consistently improving operational detection, and role-separated domain specialists outperformed global pooling on multi-subsystemtelemetry. A crash-image study across 6,599 simulated recovery states shows that detector state must be checkpointed to prevent spurious alarms and missed fault openings across restarts.