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- Experiencing equipment failures unexpected costs in production process.
- High machine downtime and low production availability
- Managers are challenged to see through all of the rapidly growing volumes of machine data captured throughout the process.
- Using Azure Machine Learning, Neal Analytics identified key variables that influence the failure of equipment such as pressure, current, and duty cycle.
- Used a classification model to predict future failures in equipment based on known failure events
- Used an anomaly detection to identify outliers in sensor data that could lead to failures throughout the production process
- Reduce total maintenance costs through better planning of predictive maintenance programs
- Able to identify potential breakdowns with 85% Precision up to two days before breakdown.
- Reduce production downtime and asset utilization
- Improve spare part supply chain planning