The Data Problem
Every turbine, pump, compressor, and conveyor in the fleet emits continuous sensor streams: vibration spectra, thermal gradients, acoustic signatures, and current draw. At fleet scale this is petabytes per quarter, spread across historians with different schemas, clock domains, and retention policies.
Anomaly detection on single signals produces noise, not forecasts. A bearing defect shows up across vibrational harmonics and thermal drift simultaneously — and only the joint pattern is predictive. That cross-modal reasoning is precisely what classical thresholding and single-signal ML miss.
The Pipeline
We wrote high-throughput streaming consumers that connect directly into OSIsoft PI and SCADA historians, normalize timestamps and units across the fleet, and window signals into fixed analytics frames. Frames route to distributed Ray clusters where frontier multimodal models fuse vibrational, thermal, and acoustic views into a single asset-health state.
Why Multimodal Matters
Single-channel models flagged roughly one in four genuine failures early; the fused model catches the joint signature. Vibration harmonics that drift upward while thermal gradients shift and acoustic profile changes — each weak alone, decisive together. Fusing across modalities raised pre-failure alert accuracy dramatically and cut false positives to a level operators actually tolerated.
Results
Operational Reality
The model's job is to buy time and focus attention. Every alert carries the joint signal evidence — which assets, which harmonics, which thermal deltas — so reliability engineers verify fast instead of hunting. That evidence-first design is why the fleet kept the system on after the pilot, not because the accuracy number was impressive on paper.