How to turn your operational data into rich insights to achieve cost benefits, improved uptime, and peak productivity

Operational technology systems like SCADA, while still in use across industries today, were designed to build digital control and remote oversight into plant operations. The data recorded by these systems holds immense value for businesses. Realizing this value, however, calls for a nimble and high-ROI pathway to log, store, and analyze this data.

Take a look at the benefits of SCADA data analytics, and how you can achieve them and improve your bottom line.

SCADA data analytics: Key benefits

When data and parameters relayed by SCADA systems is collated and analyzed with advanced machine learning (ML) techniques, it brings the following benefits for businesses:

  1. Pre-empt failures and faults: Learning from historical trends, engineers are able to spot a failure before it hits them and halts the production process. Mitigative measures can be taken in advance to ensure that the failure never occurs.
  2. Speed root-cause discovery: When an unforeseeable failure does occur, AI-assisted context analysis identifies the exact point of failure, reducing mean time to detect (MTTD) from days to hours. Moreover, each failure occurs only once and becomes a source of learning.
  3. Reduce maintenance costs: By learning continuously from past data, AI algorithms optimize maintenance schedules for each equipment. This eliminates the cost of performing preventive maintenance too often, or waiting until a failure necessitates a repair.
  4. Improve uptime and asset ROI: Optimal maintenance schedules result in better machine availability and improved efficiency, thereby maximizing the ROI on each asset in use across the span of a plant.
  5. Enhance worker safety: Equipment failure can expose onsite engineers to significant safety hazards. With process mining and real-time monitoring, AI algorithms are able to spot these risks and prescribe actions to ensure 100% adherence to best practices.

So, what does it take to realize these benefits? See below.

How to implement data analytics for SCADA systems

Implementing data analytics for traditional SCADA systems at your plant can be a complex undertaking. It typically requires a concerted effort from data scientists, plant engineers, and the oversight of a technology leader with domain expertise. Take a look at the key steps of such an initiative.

Build connectors to achieve interoperability between SCADA systems for integration

Each plant will deploy SCADA systems from multiple vendors, which are usually not interoperable. While this technology fragmentation was not a barrier for operators which were concerned with a limited set of machinery, it became a major hurdle in extracting the data relayed by these systems.

To this end, data engineers build data connectors and data pipelines are set up to log this telemetry data in a single place. Extract, transform, load (ETL) operations are performed to normalize data from multiple streams, which enables integration of SCADA systems with the analytics solution.

Build an integrated data warehouse to collect and store data from all systems

In addition to the connectors that stream SCADA data, web services are also built to synchronize SCADA databases with a modern data warehouse. If the data warehouse is hosted in the cloud, data security must be paid special attention, especially as unsecured relay opens the doors for eavesdropping, injection, and MitM attacks.

This data warehouse will store historical time series data along with the incoming data, and must therefore be built on scalable foundations. Analytics technologies like AI and ML leverage historical data to identify trends and learn from the past in a process called model training. However, the data ingested from SCADA systems will not be used as-is to train an AI model.

Cleanse and enrich data with appropriate labels and tags

In order to train an AI model, data scientists will begin by eliminating inconsistencies and labeling the dataset. Labels or tags essentially provide context to a model – for example, by teaching it to differentiate between the vibration patterns or pressure/temperature changes that precede the failure of an equipment.

Data labels are usually built to support a particular use case, whereas data cleansing must be carried out on each data set to achieve better precision in outcomes.

Identify high-impact use cases and prototype ML models

One of the most important use cases of AI in asset-intensive industries like manufacturing, oil and gas, energy, and automotive, is predictive maintenance. If McKinsey’s findings are anything to go by, predictive maintenance can curb 3-5% losses by ensuring equipment effectiveness. Other profitable use cases include process mining, cost predictive analytics, and prescriptive diagnostics.

For each of these use cases, data scientists will prototype an ML model which entails model testing, and performance optimization. If a prototype performs well, it is pushed to production. Those that are able to spot edge cases (outliers) are heralded as performant models, and will generate greater ROI for the business.

Incorporate SCADA data analytics into your operating model

Finally, the AI model is incorporated into the intended workflows – in other words, predictions must be delivered to engineers and operators in a user-friendly interface, which reduces the complexity of the process. This paves the way for modern more economical predictive processes.

Lastly, the model must be retrained and updated regularly to achieve performance improvements in the long run.

What next?

Implementing SCADA data analytics can be an expensive undertaking, and most organizations will lack the technology talent that goes into building a top-notch solution.

However, SaaS platforms like UptimeAI not only eliminate the costs associated with building and maintaining a solution, but also enable you to extract greater ROI on your existing SCADA investments. Move away from legacy maintenance and repair processes, and implement predictive analytics on your existing systems with UptimeAI to achieve operational excellence from day one. Contact us.

Subscribe to our newsletter