Asset
Improvement in Asset Life up to 25% Improvement in Asset Life up to 25%
The Belgian Maintenance Association found that when 75% of maintenance is Reactive. As per ARC group, 82% of all assets may fail at random. Until now, most of the data generated by sensors embedded in machine equipment was unused. The SCADA-based monitoring is static and rules base vs dynamic and intelligence based. At AquaML, our predictive maintenance approaches employ condition monitoring across different data sets and parameters to predict failure. By combining multiple variables and processing them through our powerful and pre-trained Machine Learning Model, we are able to predict when challenges and failures might occur, with a higher degree of confidence and fewer false positives. On Average, a local government spends $30B on O&M of Water Treatment plants. The majority of the cost is attributed towards the machine breakdowns and repairs
-Full Feature Data Engineering – 100 plus data sources
- Automated Machine Learning – More than 85 Machine Learning Model
-Zero CapEx – Flexible Pricing Options
-Customized and localized to your needs
Join over 68,000 people getting our emails
Copyright @2022 AquaML