Transforming Legacy SCADA System to Support Modern Predictive Maintenance
Transforming Legacy SCADA System to Support Modern Predictive Maintenance
About the client:
A large utilities company, located in the southwestern United States, where maintaining reliable grid performance was critical due to high demand and challenging weather conditions that often put additional stress on infrastructure.
Transforming Legacy SCADA System to Support Modern Predictive Maintenance
The client relied on a legacy SCADA system to monitor and control grid performance. While reliable, the SCADA system was incompatible with the company’s cloud-based analytics platforms, limiting access to data and preventing real-time insights crucial for proactive maintenance. The result was frequent reliance on reactive measures, where teams would respond only after equipment failures, leading to costly outages and increased maintenance expenses. The company needed a solution that preserved their existing SCADA infrastructure but enabled modern, predictive maintenance capabilities.
Our team proposed a hybrid integration solution that enabled data flow from the SCADA system to a new cloud-based data platform, with the following solution components:
| Component | Purpose | Technology used | Impact |
|---|---|---|---|
| Edge Data Connector | To fix the compatibility gap, our team implemented an edge-based data connector that could pull real-time data from the SCADA system and standardize it for cloud compatibility. | MQTT (Message Queuing Telemetry Transport) and OPC UA (Open Platform Communications Unified Architecture) protocols, for a seamless data translation from SCADA’s proprietary format to a cloud-compatible format. | This connector bridged the SCADA and cloud environments, enabling the continuous flow of data without impacting the SCADA system’s core functionality |
| Cloud Data Lake Integration | The team set up a cloud data lake to store the standardized SCADA data in a scalable format, enabling high-performance processing and storage for historical and real-time data. | AWS S3 for storage, and AWS Glue for ETL (Extract, Transform, Load) processes, which allowed for on-demand data transformation and ensured easy scalability for future data expansions | Data lake acted as a central repository, enabling data analysts and maintenance teams to access both real-time and historical performance data, driving predictive insights and facilitating ML applications. |
| Predictive Analytics Dashboard with ML Integration | To turn data into actionable insights, the team created a predictive analytics dashboard that used machine learning models to detect anomalies and forecast potential equipment failures. | Amazon SageMaker for building and training ML models on SCADA data, while Tableau was used for the visualization of real-time predictive maintenance alerts and asset health status. | With this dashboard, maintenance teams received early warnings on potential issues, allowing them to schedule preventive measures, which minimized unplanned downtime by over 40%. |
- Reduced Downtime: Predictive maintenance led to a 40% decrease in unplanned downtime, saving substantial operational costs and improving grid reliability.
- Increased Data Accessibility: SCADA data was now available to cross-functional teams, enhancing collaboration and insight generation.
- Scalability: With data housed in the cloud, the company could scale its analytics and connect additional assets without further infrastructure changes.