While attending the recent Splunk Worldwide Users’ Conference, the high level of interest in industrial machine data was apparent. To an industrial automation audience, this hardly seems surprising. But when you consider that most of the people in attendance at this conference were not your traditional industrial controls engineers looking to link a device that supports the Modbus protocol to some other device that supports the DNP3 protocol, you’ll understand why I found their interest in industrial machine data remarkable.
For me, this conference confirmed that the IT industry is truly interested in SCADA data. One of the challenges they face, however, is understanding what kind of data they can collect from an industrial control system (ICS) and what they should do with it. Another challenge for IT is the fact that ICSs are rarely, if ever, configured in the same way. They can be composed of thousands of different device types and software applications from any number of vendors. This is partly why the Kepware and Splunk partnership developed—Kepware’s software provides connectivity to thousands of different types of devices for traditional SCADA purposes.
So, just how is Splunk different from traditional ICS applications like HMIs, historians or MESs. What is the purpose of SCADA data within a Big Data application like Splunk?
HMIs, historians and MESs will continue to serve their much-needed purposes, but applications like Splunk are positioned to provide direct access to important operations data that can be used for a variety of business and operational intelligence purposes. Despite the fact that use of industrial machine data in business and operational intelligence systems will be unique for every implementation, we can still look at some general applications to see what it can mean for industry as a whole.
For example, when we look at the manufacturing process of an automobile, there is a vast amount of data that could be collected for advanced analytics. Automotive companies are competing to manufacture safe, reliable, high-performing and fuel-efficient vehicles. And because the manufacturers of these vehicles are continually improving their vehicles in both quality and features, there is added complexity and a need for Big Data analytics.
To ensure quality, manufacturers are measuring the height, width, depth and diameter of the parts produced that make up a component within an automobile. Looking at each individual component to ensure it is within the standards set forth by the manufacturer helps ensure the engine will run properly when completed. Manufacturers also trace these measured components to look for outliers, and know when to calibrate the equipment manufacturing the parts.
Using a Big Data application like Splunk, manufacturers can take data analysis a step further to look not only at the measurement of single or multiple components over time, but also at data from different sources to provide a complete view into the manufacturing process. For example, users can compare automobile service records from a dealership to the metrological traceability records generated during the manufacturing process. This information could allow manufacturers to understand where, when and what tools were used to produce the faulty components. They can then analyze the data even further by looking at similar real-time trends in the manufacturing process to set schedules for tool and equipment calibration.
Kepware’sIndustrial Data Forwarder for Splunk Plug-In is designed to ensure that Splunk has access to the industrial machine data needed to extend its view into SCADA data. The technology partnership between Splunk and Kepware enables IT and operations to converge, creating an opportunity for new types of business and operational intelligence methods that were not previously feasible.
Kepware
www.kepware.com
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