AI Predictive Maintenance for Manufacturing Equipment
Implement IoT-connected AI that predicts equipment failures 2-4 weeks before they occur, reducing unplanned downtime by 70%.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Maintenance follows a calendar schedule (every X weeks) or is reactive (fix when it breaks). Calendar-based maintenance wastes 30-40% of effort on healthy equipment. Reactive maintenance causes unplanned downtime averaging 800 hours/year. Each hour of unplanned downtime costs $5,000-$50,000 depending on the production line.
After
IoT sensors continuously monitor equipment health. AI models predict failures 2-4 weeks in advance, allowing maintenance to be scheduled during planned downtime windows. Unplanned downtime drops by 70%. Maintenance spend decreases by 25% as effort shifts from scheduled to condition-based.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Identify Critical Equipment
1 weekApply Pareto analysis to identify equipment responsible for 80% of downtime. Prioritise by: production impact, failure frequency, and maintenance cost. Select 5-10 critical assets for the initial deployment.
Deploy IoT Sensors
3 weeksInstall vibration, temperature, current, and pressure sensors on target equipment. Connect sensors to a gateway for data collection. Establish data pipelines to a central platform (cloud or on-premise). Validate data quality and collection frequency.
Build Predictive Models
6 weeksCollect 3-6 months of baseline sensor data (or use historical data if available). Train anomaly detection models to identify early warning patterns before failure. Use survival analysis and time-to-failure models for maintenance scheduling optimisation.
Integrate With CMMS
3 weeksConnect AI predictions with your Computerised Maintenance Management System. Auto-generate work orders when AI detects emerging issues. Build maintenance scheduling optimisation that considers production schedule, parts availability, and technician capacity.
Expand & Optimise
OngoingScale to additional equipment as models prove their value. Add new sensor types and data sources. Build dashboards for maintenance managers and plant leadership. Track and report on downtime reduction, cost savings, and equipment OEE improvement.
Tools Required
Expected Outcomes
Reduce unplanned downtime by 60-70%
Predict failures 2-4 weeks before occurrence
Decrease maintenance costs by 20-25%
Improve equipment OEE (Overall Equipment Effectiveness) by 10-15%
Extend equipment lifespan by 15-20% through optimised maintenance
Solutions
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Frequently Asked Questions
Ideally 6-12 months of sensor data that includes at least a few failure events. If you are starting from scratch, we deploy sensors first and build baseline models after 3 months of data collection. In the interim, anomaly detection (identifying "different from normal") can provide value even without failure examples.
Yes. IoT sensors can be retrofitted onto most equipment regardless of age. Older machines often benefit the most because they tend to have higher failure rates. The key requirement is that the failure mode produces detectable physical signals (vibration changes, temperature increases, etc.).
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