Predictive maintenance for Utah industrial and manufacturing operators
S2 Data Systems builds end-to-end predictive maintenance (PdM) solutions for Utah operators — ingesting vibration, temperature, and current signals from existing sensors, training ML models on equipment-specific failure modes, and surfacing actionable maintenance recommendations into your CMMS, ERP, or operations dashboards. Hardware-agnostic. Deployed on your AWS, Azure, or GCP account.
What we build
Sensor ingestion pipeline
Stream vibration, temperature, current, pressure, and runtime data from Monnit, Wovyn, AWS IoT, Azure IoT, or any historian into your cloud.
Feature engineering
RMS, kurtosis, FFT peaks, envelope demodulation, motor-current signature analysis — physics-aware features for ML.
Failure-prediction models
Gradient-boosted trees, autoencoders, survival models, and remaining-useful-life estimators tuned to your equipment.
Alerting & workflow
Integration into CMMS (Maximo, Fiix, Hippo), ERP (SAP, Oracle), ServiceNow, and on-call rotations.
Explainability
SHAP-based explanations, feature attribution, and dashboards so maintenance teams trust and act on predictions.
Model lifecycle
Retraining pipelines, drift detection, A/B testing of new models against incumbents.
Reference stack
Outcomes our Utah clients see
- 20–40% reduction in unplanned downtime
- 10–25% reduction in maintenance cost
- Shift from time-based PM to condition-based PdM
- Auditable maintenance decisions for ISO 55000 / regulatory frameworks
- Justified sensor expansion based on per-asset model value
Frequently asked questions
How long until predictions are actionable?
For equipment with at least 6 months of telemetry and a handful of labeled failures, useful predictions are typically delivered in 6-10 weeks. Without prior failures, S2 starts with unsupervised anomaly detection and accumulates labeled failures into supervised models over time.
Do I need to add sensors to my existing equipment?
Sometimes. Many operators already have temperature and runtime data via SCADA or historian. Vibration usually requires additional sensors (Monnit, Bently Nevada, IFM, Banner). We assess existing instrumentation first and recommend additions only where the model's accuracy depends on them.
What if my failures are too rare to train a model?
Common for high-reliability equipment. Approach: combine physics-based rule libraries with anomaly detection that identifies any deviation from learned-normal. As failures accumulate, models become supervised. Some industries (aerospace, nuclear) also benefit from transfer learning across like equipment.
Does this work for Utah operators in remote locations?
Yes. Cellular and satellite-based gateways (Monnit, Particle, Hologram) work fine for periodic telemetry. For continuous high-frequency data, edge computing aggregates locally and uploads summaries.
Related reading
Talk to S2 Data Systems
Book a 30-minute IoT data and analytics scoping call. We’ll outline a six-week proof-of-value tailored to your stack.
Book a strategy call