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
Data and Techniques we used by our pre-trained AI & ML Models
Delivering Predictive Maintenance Insights, Forecasts and Recommendations
Faster Deployment Time – Get Operational in Months vs Year
Open Platform – Ease of Integration
Value and Outcome Focused – Get ROI and Operational Efficiencies
Unique about our solution
-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
Improvement in Asset Life up to 25% Improvement in Asset Life up to 25%
Improvement in OEE by up to 15%
Operational Efficiencies including Employee Productivity by up to 15%
Process Automation improvement up to 10%
Why ESA and Machine Vibrations ?
ESA or Electrical Signature Analysis, allows us to translate the AC current and voltage data into powerful and actionable insights. The Non-invasive Sensors can be installed to capture data round the clock. In addition to ESA sensors, we can deploy noise and sound sensors. Vibration monitoring makes it possible to detect and diagnose problems before they become severe. By leveraging ESA, vibrations, and sounds, we can predict machine faults and recommend action when it makes the most sense.? By combining the data of Non-Invasive Sensors, SCADA/PLC, and the other volumetric data, AquaML is able to become your single pane of view
Hypothesis:Understand and analyze the operations and the key pain points. We build hypothesis as well as the expected benefits and outcomes.
Plant Engineering: Analyze and study the layout, the process and the design and architecture (including Machines, P&ID, Manufacturer, Aging and much more)
Exploratory Data Analysis: We start with analysis of the existing data, drive inference, anomalies and use our ML based data wranglers. At a minimum, we need three months of data. However, we always recommend one year of data. Richer the data set, better will be outcomes and predictions.
ML Modeling : We put our hundreds of Pre Trained models to work to provide high accuracy predictions and recommendations
Deploy: Deploy the model and provide insights via single pane of view for actionable insights, predictions and Model Behavior.