Solutions
Industrial IoT for predictive maintenance and early fault detection
Detect abnormal machine and process behavior before failure escalates. We adapt proven monitoring to your assets so maintenance teams get earlier warning—not just more charts.
Photo by Crystal Kwok on Unsplash
The problem
High equipment downtime is one of the most expensive problems in manufacturing and infrastructure operations. When bearings fail without warning, compressors drift out of tolerance, or pumps run until they stop entirely, the cost is not only repair—it is lost production, rushed parts, overtime, and safety risk.
Many sites still rely on scheduled maintenance or operator rounds. That means problems are often visible only after symptoms become severe. By then, secondary damage may already be underway.
Industrial IoT for predictive maintenance closes that gap by continuously measuring the signals that precede failure—vibration, temperature, current draw, pressure, runtime cycles, or environmental drift—and turning them into actionable alerts your team can trust.
How we solve it
We build condition monitoring around the assets and failure modes that matter most to your operation. Sensor selection, mounting, and sampling rates are chosen for your environment—not copied from a generic catalog.
Data flows through edge preprocessing for reliability, then into cloud dashboards where trends, thresholds, and anomaly patterns are visible to maintenance and operations. Alerts are structured for decision-making: what changed, on which asset, and what to inspect next.
The goal is not to flood your team with notifications. It is to give them time—hours or days instead of minutes—to plan inspection, order parts, and schedule work during planned windows.
- Monitoring adapted to rotating equipment, compressors, pumps, motors, and critical auxiliaries
- Vibration, temperature, runtime, pressure, and current signals based on real failure modes
- Trend views and retained history for maintenance review and root-cause analysis
- Pilot-first rollout on a narrow asset set before site-wide expansion
How we deliver it
Every engagement follows a clear path from first conversation to ongoing support—so you know what happens at each stage.
- 01
Consulting & scoping
We map your assets, failure modes, connectivity constraints, and the decisions your team needs to make. The output is a focused pilot scope—not a vague platform proposal.
- 02
Development & integration
We design sensors, edge logic, and cloud pipelines around your environment. Integration with existing PLC, SCADA, or legacy interfaces is planned from the start when needed.
- 03
Deployment & validation
Hardware is installed with minimal production disruption. We validate data quality, alert thresholds, and dashboards with your operators and maintenance team on real equipment.
- 04
Support & expansion
After go-live we tune alerts, add assets incrementally, and document what worked. You retain full control of your data and infrastructure.
Technical stack
We select hardware and software based on your environment—not a fixed vendor list. Typical components include:
- Sensors: IEPE accelerometers, RTD/thermocouple temperature probes, current clamps, pressure transducers
- Edge: industrial gateways with local buffering, Modbus/RS485 and analog I/O, optional PLC integration
- Connectivity: Ethernet, Wi-Fi, LTE, or LoRaWAN depending on site layout and interference
- Cloud: Azure IoT Hub, TimescaleDB or Azure Data Explorer for time-series, custom dashboards
- Alerts: email, SMS, Microsoft Teams, or webhook integration to your CMMS
Frequently asked questions
- How is predictive maintenance different from preventive maintenance?
- Preventive maintenance follows a fixed schedule—replace a part every 6 months regardless of condition. Predictive maintenance uses live data to detect when behavior is changing, so you act when the asset actually needs attention. That reduces both unnecessary work and surprise failures.
- Which assets are best suited for a first pilot?
- Start with assets where failure is costly and measurable: critical motors, compressors, pumps, fans, or CNC spindles. We usually recommend 3–8 assets on one line or one site so the pilot stays focused and results are easy to validate.
- Do we need to replace our existing SCADA or CMMS?
- No. We integrate where it makes sense—exporting alerts to your CMMS, feeding summary data to SCADA, or running standalone dashboards. The monitoring layer complements what you already have.
- How long until we see useful alerts?
- Most pilots produce baseline data within days and tuned alerts within 2–4 weeks of deployment, depending on asset count and connectivity. We validate thresholds with your maintenance team before treating alerts as production-ready.
- What does industrial IoT predictive maintenance cost?
- Cost depends on asset count, sensor types, connectivity, and cloud scope. We quote pilots transparently—hardware, integration, and first-year support—so you can compare against the cost of a single unplanned downtime event.
Tell us what you need to monitor, collect, or control
Whether you need early warning before failure, better process visibility, a custom device, or a cloud platform built around your workflow, we can help define the right scope.
Technical question?
Ask about sensors, integration, or deployment. We'll get back to you shortly.