Built an IoT sensor network and predictive maintenance platform across 340 pieces of industrial equipment, cutting unplanned downtime by 62% and avoiding an estimated $1.2M in annual emergency repair costs.
Halden Energy Services operates 340 pieces of rotating industrial equipment across twelve field sites, and relied entirely on scheduled maintenance and reactive repairs — meaning equipment either got serviced whether it needed it or not, or failed unexpectedly in the field, each unplanned failure costing an average of $45,000 in emergency repairs and lost production time.
Digivance designed and deployed an IoT sensor network across all 340 machines, streaming vibration, temperature, and pressure data through edge gateways into an AWS IoT Core pipeline. We then built predictive maintenance models that flag equipment showing early signs of failure — weeks before a human inspector would catch it — cutting unplanned downtime by 62% in the first year.
Rotating equipment failed without warning under the existing reactive maintenance model, each incident costing an average of $45,000 in emergency repairs and lost production across twelve field sites.
Fixed-interval scheduled maintenance meant healthy equipment was serviced unnecessarily while failing equipment between scheduled checks went undetected until it broke down.
Field technicians had no way to remotely monitor equipment health, requiring physical site visits to check on machines that might be operating normally or might be about to fail.
Several field sites had unreliable internet connectivity, making a simple cloud-only monitoring approach impractical without local processing capability at the edge.
Emergency repairs and associated production downtime were becoming an increasingly significant, unpredictable cost centre as the equipment fleet aged.
A shortage of experienced maintenance technicians meant the team couldn't simply inspect more equipment more often — the solution had to reduce, not increase, the manual inspection burden.
Deployed vibration, temperature, and pressure sensors across all 340 machines, selected specifically to detect the early failure signatures most relevant to Halden's equipment types.
Installed edge gateways at each field site to process and pre-filter sensor data locally, ensuring continuous monitoring continued reliably even during internet outages at remote sites.
Built a scalable AWS IoT Core pipeline to ingest, process, and store sensor data from all sites centrally, providing the foundation for both real-time alerting and historical trend analysis.
Trained machine learning models on historical sensor and failure data to recognise the early signatures of impending equipment failure — typically flagging issues 3-4 weeks before a traditional inspection would catch them.
Built real-time dashboards and alerting so maintenance teams see equipment health status remotely and get prioritised work orders generated automatically for equipment showing genuine warning signs.
Integrated predictive alerts directly into the maintenance team's existing work order system, so the shift from reactive to predictive maintenance required minimal change to established field workflows.
Deployed the sensor network and edge infrastructure across the first pilot site's 60 machines. First predictive alerts validated against real equipment inspections, confirming model accuracy before full rollout.
Completed sensor deployment across all twelve field sites and 340 machines. Predictive maintenance dashboards rolled out to all regional maintenance teams.
Unplanned downtime down 62% compared to the prior year's reactive maintenance baseline. Estimated $1.2M in annual emergency repair costs avoided based on early interventions triggered by the platform.
"We used to find out equipment had failed when it stopped working, usually at the worst possible time. Now our maintenance team gets a work order three or four weeks before a real problem would have shut us down. It's completely changed how we plan maintenance, and our repair budget shows it."
Let's talk about building a predictive maintenance platform for your equipment fleet.