Predictive Asset Maintenance with Digital Twin

Data

• MES data: Work order planning, maintenance, scheduling, and resource management
• Historian data
• Sensor data
• Mobility integration data
• Process data
• LIMS

Analytics

• Equipment analytics: Survival analysis, Degradation model, Proportional Hazard model
• Condition Monitoring analysis: Early alerts, time series, support vector machine, cluster analysis, classification models, vibration, oil, thermography
• Process analytics: Process models, process simulation, ID causal factors, bad actors
• Intelligence-based models: Neural, Bayesian, maintenance strategy
• RCA, FMEA, Decision tree, reliability models

Insights

• Equipment Failure predictions along with probabilities
• Optimized Maintenance Schedule
• Standard and customized reports on how the Maintenance function is performing

Actions

• Manage and create effective maintenance plans
• Raise alerts (statistical & non-statistical): Leading indicators or failure modes identified and monitored in order to intercede
• Forecast issues before they would have been detected and reduce downtime
• Prioritize maintenance based on the risk (probability) of a failure and the consequences of that failure