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Factory floor automation systems have used huge amounts of sensor data for decades to improve quality and throughput. Depending on the industry, the sensor data can be related to temperature, humidity, pressure, machine vibration, leakage, and many other things. Now, of course, modern automation systems are internet enabled and these systems can now be called Internet of Things (IoT) applications.
A couple of recent ARC Advisory Group reports focus on some issues with factory automation systems that have not received enough attention. Those issues are the same issues that all IoT applications will face. Thus, examining these factory issues can allow us to peer into the future and predict the kinds of problems that new IoT solutions will bring to enterprises.
Industrial Data Analytics Solutions Require Valid Real-Time Data, by Rick Rys, points out that when the first digital control systems were developed, it became apparent that digital controllers required data with a known quality status. This is because controllers acting on bad data could lead to costly and often perilous factory issues. Developers embedded the quality information side by side with the data as a quality status to enable the controller to know when a measured value was invalid and act accordingly. For example, standard controller behavior is to go to HOLD or MANUAL if the measurement is invalid for any reason.
However, with many control systems, it’s difficult to view the data status, making it all too common to forget about the data status when collecting data in a real-time historian, or when using that data in a calculation. Without knowing the difference between valid and invalid historical data, decisions made, or actions taken, on that data could be flawed.
“The time factor is also important, specifically the sampling frequency and the timestamping of data.” In the manufacturing industry, sample rate for data for the manufacturing application that interprets this data, the historian, is roughly an order of magnitude slower than the sensors generating the data. But there are applications where manufacturers “will need to sample data much faster than typical industrial historians.”
IoT “promises even more data coming from new sensors and often from suppliers that are not familiar with the industrial space. While these sensors can feed new data analytics applications, the industry has not yet established a common standard for how IIoT data is collected, conditioned, and communicated.”
Eric Cosman wrote about Cybersecurity and manufacturing applications in an article called Convergence of Process Safety and Cybersecurity Presents New Challenges for the Automation Profession.
Eric makes the point that the automation of industrial processes depends upon multiple engineering and technical disciplines. Regardless of the discipline responsible for automation, the skills and competencies required continue to evolve and change in the face of changing technology, circumstances and requirements.
The Automation Federation has contributed to a related competency model. This model recognizes that, in addition to the core automation skills, automation professionals also need to be versed in related disciplines such as process safety and cybersecurity. There is a growing realization that processes that have not been made secure from a cybersecurity perspective may also be potentially unsafe. This provides much of the imperative for improvements in industrial cybersecurity.
“Clearly, close collaboration between these disciplines is needed. Effective models for achieving this collaboration are still evolving, making it critical to share experiences across disciplines and industrial sectors through case studies.”
In short, just as IoT promises new capabilities, it presents us with both historical and new challenges that have still not been adequately addressed.
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