These trends have been driven by available technology and sophisticated end users who want the same flexibility and convenience offered by consumer applications. However, the path to merging OT with IT has had a few bumps and potholes.
The Industrial Internet of Things (IIoT) relies on well-coordinated products and performance in the OT and IT realms. For the progressively overlapping roles of OT and IT to be most effective, it is necessary to merge the strengths of OT and IT disciplines. It’s useful to examines what those strengths are, and how a hybrid OT/IT solution approach can become greater than the sum of its parts.
OT and IT seek to expand constraints
A reality is work performed in OT and IT environments are subjected to unique constraints. One key challenge is traditional hardware and software solutions used by OT and IT evolved from different starting points. They are used by distinct groups of people whose objectives and skill sets were not the same. OT and IT specialists often find themselves out of their element when exposed to common tools used by the other group.
OT tools for automation expand with IT
OT tools emerged for domain experts, primarily with electrical or mechanical engineering backgrounds, for the primary purpose of monitoring and controlling production equipment with the utmost reliability.
Automation designs and physical processing equipment are often capacity-constrained and require control engineers to apply intimate knowledge of the limits. Ladder logic, structured text (ST), and function block diagrams (FBDs) are examples of specific industrial development languages used by OT communities to manage programmable logic controllers (PLCs), distributed control systems (DCSs), supervisory control and data acquisition (SCADA), historians, and other typical OT applications.
IT tools emerged at the other end of the spectrum with software and computer engineers often in mind. More recently, additional personnel such as cloud developers, data engineers and data scientists have become involved. Cloud systems, by their very nature, assume infinite compute and storage capacity. The idea of constraining the application to a small edge device is as foreign to the IT community as developing a C# or Java application is to some OT experts.
The need for insights from a common data set is forcing a convergence of the tools used to collect, distribute, and analyze data – requiring the use of OT and IT applications. Initially, IT-oriented developers assumed all analysis would be moved to the cloud, where a vast open-source community has created a collection of tools well suited to analyzing large data sets. Many corporate initiatives were launched with this objective in mind, often with limited or mixed results.
OT applications generate an enormous amount of “little data” from machines operating 24/7/365. In aggregate, one industrial facility can create “Big Data” significantly exceeding consumer applications, even those with millions of users.
Certain OT applications like historians have evolved to efficiently store this data through built-in compression algorithms and very high-speed indexed data access. Moving this volume of data to the cloud and storing it there is cumbersome and expensive. Typical cloud software tools are not as efficient in storage and access speed, so interactions with cloud-based data can be less than acceptable for evaluating even one machine’s data set.
On the other hand, IT tools are more efficient at analyzing large data sets across a fleet of assets by using parallel computing techniques built into the software infrastructure. Addressing performance challenges is often as simple as adding an additional server to the system. Likewise, IT infrastructures are designed for supplying information to a full set of users because they incorporate browsers, web servers, and mobile devices to provide information to people regardless of physical location.
A final point is OT applications often need to be always available because any outages would cause equipment and production downtime. IT-based cloud applications, on the other hand, can often run in a partially-connected paradigm, taking advantage of available computing and connectivity resources.
Converging edge/cloud tools, platforms
To deal with the challenges discussed above, development tools are merging the best aspects of OT and IT, with a shift toward edge-located platforms used either standalone or in conjunction with cloud-based efforts. Out of necessity, OT and IT applications and users are now sharing edge platforms, which have emerged as a crucial element for industrial data management tasks.
IT users understand the benefits of executing analytics at the edge with high fidelity and low latency mission critical data designed and architected to always be available. For plant-wide, multi-plant, and fleet-level analysis, data must be transmitted to higher level systems. Edge platforms are well suited to delivering the proper subsets of data from each site to cloud-based platforms, where massive parallel compute architectures can be used to gain big data insights. When connection is lost to the cloud-based systems, the edge platforms typically store the data locally until connectivity is restored.
OT users also are adopting more commonly IT-oriented development tools such as Node-RED and Python, which are now available to run on edge hardware. They are using these and other tools to develop OT-focused applications for reducing downtime, predicting failures, and improving production efficiency. These tools are more powerful than traditional OT options for analytical and dashboarding applications, and they are enabling the creation of advanced IIoT applications.
Evidence of this level of IT/OT platform convergence is seen with the emergence of IT protocols at the edge such as message queuing telemetry transport (MQTT), advanced message queuing protocol (AMQP), and the more recent adoption of OPC UA, a traditional OT protocol, for delivering production data to IT applications.
Edge hardware will continue to evolve to support OT and IT users who want the best of both worlds for developing, deploying, operating and optimizing systems. For example, classic OT tools allow dashboard graphical objects like an RPM gauge to be dragged, dropped, linked, and moved as needed, but within relatively restrictive circumstances. In the IT world, the developer can create such an object in any way they want, but it often requires customized effort.
A hybrid evolution must make accessing all these elements easier for OT and IT personnel. It should be tailored for OT engineers to use while incorporating necessary IT advancements to quickly get information to the right people regardless of where they are. OT systems provide the best operational reliability but are not ideal for more advanced computing like fleet level analytics. IT systems are best positioned for analysis purposes and can span multiple OT installations. By seamlessly sharing edge devices in a cloud/edge infrastructure, OT and IT analysis can be performed on the same data.
Easy accessibility must be addressed for OT and IT personnel so all disciplines can be comfortable working with the hardware, software and networking technologies involved. This also includes the ability to rapidly create, use and re-use IIoT applications combining OT and IT principles.
Edge products and tools need to retain the robust operating features of traditional systems used by OT to automate equipment and connect with input/output (I/O) while being usable by plant operational and maintenance personnel. However, managing edge applications at each individual point-of-use is a problem.
To address this issue, IT concepts can be leveraged so edge applications are centrally managed like IT applications, allowing them to be kept current with the latest software releases and security updates and then easily and automatically distributed to the edge nodes where they are used. Comprehensive and seamless access to IT-centric features like networking, cloud processing, and advanced computing and visualization options is a must for meeting these challenges.
Reconciling OT, IT: 3 edge automation examples
In the consumer world, some software products and services are carefully curated and secured, while others are openly distributed. The former is better defined and more protected than the latter, but it is generally more costly and less flexible. Both approaches can be desirable, and sometimes they are even combined.
Similarly, industrial automation platforms and tools are available today with sufficient rigor for OT, and plenty of freedom for incorporating IT technologies. Three examples are:
Edge controller: Provides reliable hardware, similar in many ways to traditional PLC technology, but extensible to enable general-purpose computing (Figure 3).
Edge stack: There are many software options in the IT world, but a carefully curated stack of software elements most useful for edge projects can significantly streamline IIoT implementations.
Edge networking: Device-level networking – such as industrial Ethernet, OPC UA, and other specific industrial protocols – are necessary for gathering source data. Other more IT-friendly protocols, such as MQTT, are required to efficiently communicate data to higher level, IT oriented systems. Although networking architectures are flattening, edge computing plays a key role in coordinating both types of networking.
As more end users embark on a digital transformation journey, they need the right platforms and tools to build IIoT applications. A successful OT and IT evolution creates a seamless merger and results in an edge solution where technology is abstracted from usage, providing end users with the right balance between easy access and expanded functionality.
For information about how Radwell International can assist your operation
Join Radwell Automation Nation