Phase 1
Data and failure definition
Assess sensor coverage, labels, maintenance history and business value.
Projects · Machine Learning
Predictive Maintenance
A data and machine-learning system for identifying abnormal equipment behaviour and supporting maintenance decisions.
Project overview
Unplanned equipment failure can create downtime, cost and safety risk, but useful prediction depends on data quality and a clearly defined failure or degradation signal.
The project demonstrates how sensor histories, maintenance records and operational context can be prepared, modelled and monitored.
The system compares practical baselines with more advanced methods rather than assuming that a complex model is automatically better.
Technology and architecture
Time-series data pipelines
Anomaly detection
Classification and forecasting
Model monitoring
Maintenance dashboard
Potential value
Earlier indication of equipment problems
Evidence-based maintenance prioritisation
Reduced avoidable downtime
Reusable industrial analytics capability
Roadmap
A staged route to evidence.
Phase 2
Model and dashboard
Develop baselines, evaluation and operational visualisation.
Phase 3
Pilot monitoring
Validate alerts, drift, false positives and maintenance usefulness.