Enabling Industry 4.0

Predictive Maintenance

Provides earliest indication and deliver actionable insights to improve maintenance management
Advanced Analytics
Mining, Power, Oil & Gas
In every production facility of any capital-intensive industries, unexpected failure and downtime of equipments can bring the whole operation to a halt, and cause major business problem.
Aspiration
Being able to predict equipments or machines failure before it happens will help company to reduce maintenance and repair cost significantly. And it also help them to plan better for sourcing crucial parts.
Approach
We collect and integrate maintenance-related data from multiple sources like inspection data (oil sampling, visual inspection report, etc.), sensors data (vibration, pressure, temperature, voltage, RPM), and maintenance post-event data (damage classes, maintenance types, planned v.s. unplanned events, maintenance actions, and maintenance subcomponents. Then with the help of machine learning model, we combine these data to produce estimation of failure mode probability, distribution of repair cost and severity of failure, and prediction of detectability and time to failure.
Outcome
  • Provide accurate data capture
  • Alert on potential machinery breakdown
  • Manage better planning for production and maintenance
  • Manage better planning for spare part inventory
Next Use Case
Line Balancing & Optimization
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