Failure-prediction models, anomaly detection, and automated recommendations — production-ready ML pipelines tuned to your equipment population, deployed where the work happens, and observed in MLOps from day one.
AI-driven asset intelligence is the third pillar of Droz's reliability practice — the layer where the field program (Pillar A) and the data platform (Pillar B) compound into automated decisions. Droz designs and trains failure-prediction models against your specific equipment population — not horizontal models retrofitted to industrial data. Anomaly detection runs continuously on streamed signals; automated recommendations land in the operator's existing channels with the underlying evidence attached. Models are deployed through production-ready MLOps pipelines on Azure ML, AWS SageMaker, or Vertex AI — versioned, monitored, and re-trained on a cadence calibrated to drift. Where the engagement justifies it, Droz also builds a digital twin of the plant — a simulation surface that lets operations rehearse decisions before they are taken in the real plant.
Supervised models trained on labeled failure history per asset family — bearings, motors, pumps, gearboxes. Outputs: remaining-useful-life estimate, failure-mode probability, recommended intervention window. Re-trained on a cadence calibrated to drift.
Unsupervised models running continuously on streamed signals (vibration, temperature, current, ultrasound, energy). Outputs: severity-graded anomalies routed by criticality with the underlying signal evidence attached.
Decision models that combine condition signals, operating context, and economic constraints to recommend the best next action — schedule the intervention, adjust the operating point, re-order the spare.
The MLOps and data-science engagement that builds and operates these models → AI & Machine Learning Systems
Field history from Pillar A and the unified data layer from Pillar B are curated into labeled training sets per asset family. Class imbalance, leakage, and label quality are handled before any model touches the data.
Model class is chosen per failure mode — gradient boosting for tabular condition history, deep models for spectral signatures, isolation-forest variants for streaming anomaly. Hyperparameters are tuned in a managed sweep.
Models are validated on holdout sites and replayed against archived failures — the model has to predict a known failure on data it has never seen before it ships.
Deployed through Azure ML, AWS SageMaker, or Vertex AI — endpoint, batch, or edge depending on latency. Inference is logged with the input snapshot for traceability.
Production performance is monitored — concept drift, data drift, prediction-class drift. Re-training is triggered on threshold or on schedule; the previous model is held in shadow until the new model is greenlit.
A Droz digital twin is a simulation surface for a plant or process train — calibrated to the asset population, fed by the reliability data layer, and used to rehearse decisions that are costly to take in the real plant. Built per client; not a horizontal product.
Asset behavior modeled at the level required by the decision — first-principles physics where the decision is operational, data-driven where the decision is reliability.
Run “what if this bearing fails on Friday” against the production schedule, the spare-parts position, and the corporate uptime target. The output: a ranked decision tree, not a single answer.
Test capital deferrals, intervention-window changes, and operating-point shifts in simulation before committing the real plant to them.
ML pipelines and inference endpoints run inside the same SOC 2 Type II audited control set as the reliability data platform.
Anomaly thresholds for rotating machinery models are anchored to ISO 10816 zone limits — models flag deviations relative to the standard, not arbitrary baselines.
Pipelines deployed on the client's existing cloud — Azure, AWS, or Google Cloud — through Droz's certified partner agreements.
Full list of certifications and associations → · Cloud & AI partners → · Underlying AI practice (LLMs, RAG, evaluation) →