A US based industrial organization offering industrial products to a large customer base.
- Which are the indicative parameters for industrial pump failures? How to optimize these parameters?
- Real-time monitoring of the functioning of pumps for critical alerts.
- How to predict the likelihood of pump failures?
- How to optimize the maintenance costs?
- Exploratory Data Analysis: We analyzed last 3 years data for around 5000 pumps for last 3 years. We measured the correlation between downtime and aberration for different parameters.
- Real-time monitoring: With time-series analysis and forecasting we built accurate real-time monitoring of pumps to identify: 1) Normal behavior of each operating parameter 2) Anomaly detection in each operating parameter. Using this analysis, we created an alert generating mechanism.
- Predictive modelling: With all the correlating parameters and anomaly detection data we built an advance classification model to predict the failure of pumps.
- Recommendations: We also charted the recommendations and prescriptions for all the relevant stakeholders to deal with the alerts and predictive failure data.
- Instead of relying on scheduled maintenance, now we could maintain pumps for their uptime.
- Real time monitoring and predictive failure analysis saved 1500 hours of downtime.
- Savings of $100K per month in the maintenance costs.
Predictive maintenance helped this Industrial firm to save almost $100K/m for each asset in maintenance costs.