New hardware and software systems for remote monitoring and predictive maintenance promise to reduce downtime, human error, and cost by improving early detection and automating maintenance procedures. However, for many users, several obstacles remain to achieving this outcome:
- Equipment assets are not sufficiently instrumented
- Field instruments are not connected to automation networks
- Automation networks are divided into data silos
- Automation networks and business networks are not integrated.
The underlying issue behind many of these obstacles is the limited scope of communication of traditional automation devices, like programmable logic controllers (PLCs), and the complexity this creates in data systems. Given the significance of remote monitoring to enable other Industry 4.0 goals, automation vendors are bringing new technologies to bear on the problem. In combination, these technologies can simplify equipment integration and data acquisition, making it possible to send data directly from the plant floor to the cloud.
Understanding the roots of complexity
The complexity of operations technology (OT) becomes apparent every time a technician or engineer wants to bring another signal into the system. First someone has to identify the right I/O module type to use. Then someone (probably a different person, depending on the size of the facility) has to install the appropriate enclosure and wiring.
That I/O module is usually installed in a controller or gateway that then needs to be configured to scan the I/O and log data. And more than likely, that controller can’t send I/O data to its ultimate destination, so an open platform communication (OPC) server or some other middleware has to be configured to scan the controller. Maybe there is a supervisory control and data acquisition (SCADA) system that also scans the controller, but a different piece of software is probably required to access, format, filter, and prepare that data before it can be added to a database or analytics platform somewhere (Figure 1).
How many people does it take to put the whole puzzle together, and what does that do to a project’s timeline and budget?
This complexity is the result of automation devices that weren’t designed with interoperability or security in mind. The traditional control system relies on proprietary, point-to-point communication protocols designed mostly to move data from slave devices to a master controller or application. But the lack of interoperability between devices using different protocols or network media means OT data remains trapped in disparate networks unless bridged by middleware. The lack of security further adds to the complexity, because information technology (IT) personnel need to take measures to secure data after the fact with a plethora of firewall rules or network segmentations that complicate data acquisition.
Reducing complexity with edge computing
While data siloing has become the status quo for industrial applications, the consumer and enterprise sectors have experienced an explosion in the number of connected devices in recent years. The approach that IT experts have developed for designing networks in response to this change is called edge computing. This distributed architecture places lightweight computing resources at the local network level that help to process and transmit data at its source, improving local responsiveness and increasing the efficiency of data transfer to central computing resources and data consumers.
Applied to automation networks, edge computing places more connectivity and processing power on the plant floor, which can be used to facilitate integration so that moving data across the organization no longer requires a deep technology hierarchy. With embedded support for multiple OT and IT protocols, industrial edge computing devices can process data from sensors and transmitters on the plant floor, then send it directly to on-premises or cloud-based applications and databases without intermediary hardware or software (Figure 2).
Importantly, industrial edge computing devices also embed modern IT security standards to protect equipment from outside intrusion and safely transport data across public networks. Embedded features like user authentication, device firewalls, and data encryption can reinforce existing network protections or be used to further consolidate the technology hierarchy.
Many edge computing offerings are appearing in the industrial automation space, most commonly in the form of edge I/O and communication gateways. These provide a variety of methods for connecting devices and equipment, including traditional wired sensors, to form unified data networks. Edge programmable industrial controllers (EPICs) are a more powerful option that combines the real-time control and I/O sensing capabilities of PLCs/PACs with a broad array of connectivity tools.
Industrial edge devices provide the basic connectivity and data processing needed to establish a communications backbone within automation networks and between OT and IT systems. They combine essential communication interfaces, protocols, and applications in a single connectivity layer available to the local process, rather than spread across multiple layers of costly middleware.
Building bridges with the IIoT
Of course, hardware is only as good as the software it runs, so in addition to the basic framework provided by the edge computing layer in this new communication model are a host of related tools designed for the industrial internet of things (IIoT). This software layer operates within the secure ecosystem created by a network of industrial edge devices and is focused on using hardware resources to extract, transform, and move data efficiently from plant floor sensors to data consumers in the organization.
While there are many options, a few key technologies are worth exploring in detail.
Node-RED. Originally developed within IBM before being open-sourced, Node-RED is a low-code, visual IoT programming language for transporting data across many protocols and web APIs (application programming interfaces). It provides a large library of functions for building data flows that work intuitively with different connected devices, a wide variety of databases, and cloud IoT platforms.
OPC UA. Traditionally a component of automation middleware requiring a Microsoft Windows environment, the OPC standard has become available to edge devices via the platform-independent OPC unified architecture (UA) specification. OPC UA allows an industrial edge device to run an OPC server and communicate with legacy PLCs directly using their native protocols. This critical integration component makes it possible to deploy an edge communication network in parallel with existing automation. Rather than requiring the usual rip-and-replace approach to modernizing infrastructure, legacy equipment data can be assimilated across disparate networks and data silos without interrupting operations.
MQTT with Sparkplug B. Another open-source contribution from IBM, MQTT has become the most popular IoT-specific communications protocol for its flexibility and efficiency. In an MQTT network, edge devices need to connect to only one endpoint: the broker. The broker can then share data to as many authorized systems as desired. For example, equipment data can flow from the plant floor to a local maintenance database, to a SCADA host, and to a cloud analytics system simply by pointing each application to a shared MQTT broker. Edge devices that comply with the Sparkplug B companion specification guarantee interoperability, low tag management, and mission-critical state awareness.
Bringing it all together
The new approach to plant integration can be described in three steps. First, industrial edge computing devices form a secure, connected OT foundation, bypassing layers of traditional middleware to connect the plant floor to the rest of the organization. Next, OPC UA forms a bridge between the edge layer and legacy devices without disturbing existing automation. Finally, IoT protocols, like MQTT, integrate edge data into a secure, many-to-many data sharing architecture. This approach creates a unified data network between the plant floor and software applications on-premises or elsewhere in the organization that allows for remote monitoring of new and existing equipment assets without the obstacles of traditional control systems.
This article is part of our monthly Automation Zone column. Read more from our monthly Automation Zone series.