Actionable Data Analytics for Control Engineers

July 27, 2016
Just as modern data analytics can support actionable business insights, appropriate actions based on analysis of sensor-based data can provide actionable insights in the industrial process control world.

Human failures and limitations were no doubt a motivating factor for developing automated control systems. Control systems can act reliably, fast and effectively 24/7. Pneumatic and hydraulic actuators or solenoids and relays can take real physical actions. These actions originate from sensor data like temperature, pressure, level, flow, rotational speed, limit switches and many others. The data are acquired in real time by control or safety systems and algorithms are programmed to take appropriate actions when needed. Like data analysts, process control engineers work with inputs, algorithms and outputs to produce appropriate actions.

Control systems are indispensable for modern manufacturing. Operators in the process industry typically have responsibility for 90-180 control loops each. These control loops are constantly moving actuators much quicker and more effectively than 90-180 humans could move them. And control systems never get tired.

Control systems are akin to actionable data analytics in real time. There is no doubt about the high value that this technology has provided in the process and discrete manufacturing industries.

Process control vs. data science

Conceptually, the process control engineer is very much like a data analyst. Both use data to instigate actions. In practice, however, the roles differ in a number of ways. Control engineers create calculations (algorithms) that move physical devices in the manufacturing plant. In contrast, data analysts often create calculations that guide human actions for asset management and business support. Data analytics can support enterprise resource planning (ERP) or other business systems and influence supply chain activities.

Control engineers not only accept the data they work with; they also specify the instruments they need to make the control system regulate the process properly. Control engineers also agonize over how to act when sensors or communications misbehave, so they focus carefully on the data quality and the ways that sensors could fail. Data analysts, on the other hand, are typically not involved with specifying field instruments and may have limited information on data quality.

Process is inseparable from process control. Control and safety system algorithms are developed based on a fundamental understanding of the process. Data analysts tend to learn the process from the data, but prior process understanding certainly helps.

While control engineers typically work with real-time data, data analysts may need to work with both transactional and real-time data. Real-time streaming data are distinctly different from transactional data or the unstructured data gathered from mobile, social, video and cloud computing applications. The software used to store and analyze each is markedly different. Real-time data tend to be stored in a simpler real-time database optimized for the task. There are many NoSQL or non-relational databases. MongoDB, HBase and Cassandra are examples, although control system suppliers have often written their own database application for collecting real-time data. Many new data sources and file types, such as video and 3D, do not really fit well into either transactional or real-time database structures.

Positive actions depend on good data

Control engineers tend to work from the actuator back to the sensors based on an understanding of how an actuator affects the process. The control engineer has a process model in mind to identify actionable decisions during control strategy design. This is where data analysts can learn from the control and process engineers. A good first step for a data analyst could be to identify valuable actions that might be taken. This means the data analyst should have a model in mind about how analysis of the data interacts with the output target. That target could be operational, maintenance-related, or related to the business system.

In many instances, it would be a good idea to add specific sensors that could improve analytic calculations. For example, adding vibration sensors could greatly increase the odds for predicting machine failure and allow the analytics to instigate appropriate maintenance actions.

We’ve heard estimates that data analysts spend as much as 80 percent of their time inspecting, cleaning and transforming the data. Many of the newer manufacturing intelligence tools focus on making this easier. Typical tasks can include filtering out bad or irrelevant data; sorting data based on events, context or equipment; and synchronizing the timing of events.

Data collected and stored in real-time repositories arrive via different communications interfaces. These could include standard interfaces like OPC UA or custom application program interfaces (APIs). Not all data available in the control system are collected in real-time database repositories. These repositories can be the main source of data for many data analytics applications. The stored data might lack some of the sensor quality information depending on how the data collection is configured. Industrial Internet of Things (IIoT) sensor data might arrive at the data repository without the full suite of data quality status bits you often would see with sensors connected to the control system. In some cases, data analysts could be working with bad data that is not marked as such.

Analytics tools can help, but understanding how data get from the sensor to the data analyst’s tools is essential to improve data inspection, cleaning and filtering. Ideally, the data analyst should be able to spend more time conceiving and building useful calculations and models, and less time inspecting and managing the data.

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