Predictive Maintenance in Manufacturing: A Practical Guide

Unplanned equipment downtime costs manufacturers an average of $260,000 per hour. For our client — a mid-sized automotive components manufacturer — unplanned downtime was costing over £4M annually. Here's how we reduced it by 38%.

The Problem

Their maintenance strategy was purely reactive: machines broke down, engineers responded. There was no visibility into equipment health and no way to predict failures in advance.

The Solution Architecture

Data collection: We instrumented 47 critical machines with IoT sensors measuring vibration, temperature, pressure, and current draw. Sensor data was ingested via MQTT into a time-series database (InfluxDB).

Feature engineering: Raw sensor readings were transformed into features: rolling means, standard deviations, rate of change, spectral features from FFT analysis of vibration data.

Model training: We trained an XGBoost classifier on 18 months of historical data labelled with failure events. The model predicts failure probability over the next 7 days with 84% precision and 79% recall.

Alerting and dashboard: A Grafana dashboard shows real-time equipment health scores. Alerts trigger maintenance work orders in their CMMS when failure probability exceeds a threshold.

Results After 6 Months

- 38% reduction in unplanned downtime - £1.5M annual savings in maintenance costs - Maintenance labour efficiency improved by 22% - Average time-to-repair reduced by 45 minutes per incident