Equipment analytics and failure root-cause analysis, built around lead time rather than detection accuracy. A model that detects a failing bearing four hours out has told you what the noise already had; one that flags it three weeks out changes how the plant is run.
Supervised fault classification needs labelled failures, and a well-maintained plant does not produce many. Three bearing failures in five years is not a training set.
So the practical framing is anomaly detection against a healthy baseline, combined with physics. Bearing defect frequencies are calculable from geometry and shaft speed, so a rising component at the outer-race ball-pass frequency is interpretable without ever having seen that failure on that machine.
Physics gives interpretability; learned models give sensitivity to patterns the physics does not enumerate. Used together they produce alerts a maintenance engineer will believe — which is the binding constraint on whether the system is used at all.
| Parameter | Detail |
|---|---|
| Modalities | Vibration, thermal, motor current, acoustic and process data |
| Framing | Anomaly detection against a healthy baseline, not supervised fault classification |
| Interpretation | Physics-based defect frequencies alongside learned models |
| Objective | Actionable lead time, not detection accuracy at the point of failure |
| Operating states | State-aware, so load variation does not generate nuisance alerts |
| Integration | CMMS, so alerts become work orders |
| Typical markets | Manufacturing, energy, process, utilities |
Maturity — silicon-proven, FPGA-validated or RTL stage — is confirmed at enquiry for the specific configuration you need, rather than claimed generically here.
Send the target node, the interface requirements and the integration context. If this is not the right fit, that will be said early rather than discovered at integration.
Less than expected, because the framing is anomaly detection against a healthy baseline rather than supervised fault classification. Physics-based defect frequencies make alerts interpretable without prior failure examples on that machine.
Because a machine at forty per cent load is not the same machine at ninety per cent. Comparing measurements across operating states without accounting for them generates nuisance alerts, which is how these systems get switched off.
Lead time, evidence and routing. Enough warning to schedule the intervention, the spectrum and trend so an engineer can agree or disagree, and delivery into the maintenance system rather than an inbox.
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