Precision Manufacturing
Reduction in total maintenance costs
Improvement in first-pass quality rate
About
A precision manufacturer running four facilities on a calendar-based maintenance schedule that missed the failures that mattered and over-serviced the equipment that didn't need it. We built a condition-based maintenance model and a real-time quality monitor from the same sensor infrastructure.
Industry
Precision Manufacturing
Company size
1,000 – 5,000 employees
Founded
1995
The Company
Precision manufacturing with a maintenance process that had not kept pace
The manufacturer operates four production facilities, each housing 20 to 40 pieces of complex industrial equipment producing precision-engineered components for industrial and automotive customers, where quality defects carry significant downstream cost.
Maintenance was historically calendar-based: equipment serviced at fixed intervals regardless of actual condition. This prevented some failures but generated unnecessary maintenance on healthy equipment while missing failures that developed faster than the maintenance cycle anticipated.
The challenge
Two parallel problems: unplanned failures and late quality detection
Unplanned equipment failures between scheduled maintenance events caused production disruptions averaging 14 hours per incident. In parallel, quality defects were often caught too late — at final inspection or post-shipment — generating rework costs and occasional warranty claims.
Sensor data had been collected for years but sat in log files reviewed manually only when investigating a known problem, rather than monitored continuously for early warning signals.
The Solution
Predictive maintenance and quality monitoring from a single sensor pipeline
TSFresh transformed raw sensor time series — temperature, vibration, current draw, runtime, pressure — into features capturing both current state and trajectory. A LightGBM classifier trained on historical sensor data matched to maintenance records identified the patterns that preceded failures, connected to AWS IoT Core for real-time ingestion and pushing a daily health score with automated alerts.
The same infrastructure fed a real-time quality anomaly detector monitoring process parameters during production runs, flagging deviations before they produced defective output, with alerts routed to line supervisors through a Grafana dashboard that could trigger automatic hold flags on affected batches.
The Results
22% lower maintenance cost, 16% better first-pass quality
Total maintenance costs fell 22% through reduced emergency repairs and a shift from fixed-calendar to condition-based scheduling. First-pass quality improved 16% as the monitoring system caught process deviations in time for corrective action during the run rather than after it.
Unplanned maintenance events fell 34% in six months, with the model correctly flagging developing failures in 78% of monitored equipment ahead of the scheduled maintenance window. Mean time between failures improved 2.4x.
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