Hybrid-AI Predictive Maintenance for Industrial Equipment by Preston Johnson

Join Preston Johnson as he presents this exciting webinar

Industrial predictive maintenance is entering a hybrid era. Physics-based failure-mode models — grounded in FMEA/FMECA and decades of practitioner expertise — tell us precisely which faults to hunt and which sensors reveal them. Data-driven AI — anomaly detection and advanced pattern recognition — catches the deviations physics alone can’t anticipate. This session shows how to fuse the two into an explainable, “causal AI” approach that detects more failure modes earlier while keeping reliability engineers in command of the diagnosis. Using real deployments from power generation, water, and chemical processing, we’ll walk the full implementation path — from failure-mode selection and sensor mapping, through AI-guided diagnostics, to prescriptive action — and show how a hybrid diagnostic layer turns an alert backlog into a single, ranked, evidence-backed diagnosis that supports outage planning.

Learning Takeaways:

1. Physics first: how FMEA/FMECA-driven diagnostic models map faults to symptoms to sensors — the disciplined “start with reliability, not sensors” approach.
2. Data-driven detection: how Advanced Pattern Recognition baselines symptoms per operating state and weights anomaly residuals — and why weighted-sum residuals outperform single-sensor alarms.
3. Explainable hybrid models: why fusing the two yields “causal AI” you can trace from a ranked diagnosis all the way back to the raw waveform — no black boxes.
4. Human-in-the-loop: positioning reliability engineers as the validators who train and refine the models — a core trust and risk-mitigation strategy.
5. Business outcomes: proven ROI from hybrid deployments at scale, including a 6× increase in risk detection and $170M in documented savings.