Aingura insights
Preprocessing
Transformation
Data Acquisition
Sectors
Utilities
Applications
Consumption Efficiency
Maintenance
impact
- Generation of an explainable model that represents the dynamic behavior of the electrical and mechanical system of the pump.
- Use of the estimated modal parameters to generate a probabilistic machine learning baseline that produces a KPI of the healthy state of the pump’s operation.
- Use of high-speed measurement of power consumption (8 kHz) to analyze both mechanical and electrical behavior, minimizing the number of sensors required and increasing their robustness to the environment
Developed AI Technologies
- Machine Learning-based electromechanical baseline.
- High-speed KPI generation for highly dynamic systems.
Challenges
- Avoidance of unscheduled shutdowns, mainly to avoid interruptions in the supply of drinking water.
- Improvement of energy consumption.
- Development of maintenance procedures based on behavioral deviation analysis to avoid corrective maintenance and minimize preventive ones
results
- Reduction of unscheduled shutdowns (>85%), allowing the reduction of the redundancy pumps.
- Reduction of preventive maintenance cost (50%)
- Reduction of energy consumptions (8%).
- PRI: 2 years
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