Phase 1
Data and failure definition
Assess sensor coverage, labels, maintenance history and business value.
项目 · 机器学习
预测性维护
A data and machine-learning system for identifying abnormal equipment behaviour and supporting maintenance decisions.
项目概览
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.
技术与架构
Time-series data pipelines
Anomaly detection
Classification and forecasting
Model monitoring
Maintenance dashboard
潜在价值
Earlier indication of equipment problems
Evidence-based maintenance prioritisation
Reduced avoidable downtime
Reusable industrial analytics capability
路线图
分阶段验证路径。
Phase 2
Model and dashboard
Develop baselines, evaluation and operational visualisation.
Phase 3
Pilot monitoring
Validate alerts, drift, false positives and maintenance usefulness.