Extreme OT Data Makeover

Summary

Industrial digital transformation had its beginnings in the Industry 4.0 initiative, unveiled in Germany in 2013. Since then, industrial digital transformation has morphed into an imperative that underpins business vitality. However, according to the 2020 Industrie 4.0 Maturity Index in Industry released by the German National Academy of Science and Engineering (Acatech), more than 90 percent of businesses are still only in early stages of industrial digital transformation.

Most firms are still grappling with how to record and aggregate data generated by their operational technology (OT), equipment, and workforce. Such OT data is still far from delivering what the decision makers at most businesses initially hoped for. Their vision is having big data analysis results—completed and readily available—right on their screens, so that business insights can be implemented to lower cost, increase efficiency, or drive business model innovation. Companies still have a long way to go to get industrial digital transformation to this level.

Why are companies still behind in the industrial digital transformation race? One of the main reasons is that big data analytics, artificial intelligence (AI), and other innovative technologies are only implemented in the later stages of digital transformation. But if you do not obtain enough data in the early stages, then even the smartest AI or machine learning solution will have little value for you.

As we know, in the industrial digital transformation landscape, data comes mostly from OT environments—for example, a drilling well in the middle of a desert baking at temperatures of 40°C to 50°C, an oil pipeline system stretching for hundreds of kilometers in a freezing area, or a transportation system of a fast-moving and vibrating train. It does not require a huge stretch of one’s imagination to perceive how difficult it is to capture data from these harsh environments.

Hence, kick-starting a transformation initiative first requires a failproof strategy on how to accurately capture OT data from industrial automation equipment. Furthermore, this issue requires deep thinking. OT data has shifted from monitoring-oriented to optimization-minded—looking not just at the present but also to the future. Errors in data collected from its sources may lead to defects in subsequent analyses.

Therefore, the focus can longer be just capturing “stable data.” Overcoming obstacles to quality data is the deciding factor of any transformation program’s success. Industrial automation and business professionals need to pay attention—early in the digital transformation process—to four key pillars of data quality: acquisition, preparation, transmission, and security.

Challenge: Insufficient data

One of the challenges to data quality is insufficient data. This is mostly because automation systems were not designed for data analysis. Even in cases of data transmissions on a shop floor, data is tapped to support control equipment operations only, which is not enough by any measure for distilling business insights.

For example, a factory may have a bottleneck machine on its production line through which everything gets made or processed. If the machine goes down, the entire line shuts down. To minimize downtime, the plant needs to predict which key components inside the machine could fail and purchase the replacement components in advance. However, these devices seldom provide data regarding their key parts and components. Therefore, the company needs to install sensors in them and convert the generated analog signals to digital ones via remote I/Os. The digital signals can then be sent to servers in the upper layer or to the cloud to enable predictive maintenance. This demonstrates the capability, or attribute, of OT data acquisition.

In this scenario, only one machine needs to be worked on. If you are dealing with an entire factory with myriad communication protocols, it goes without saying that the complexity of conversion will be much greater. Because OT systems are typically used for a few decades or more, equipment from various vendors is often applied in the same system. Moreover, each piece of equipment has its own proprietary hardware design, communication interface, and communication protocol to deliver operational availability.

If the equipment works independently, this silo approach is effective for ensuring system reliability and optimal performance. However, data silos will have formed over time. For instance, two production lines in the same plant may use different programmable logic controllers (PLCs) from two different vendors, each with its own communication language for the respective PLC. When seeking to aggregate data from different systems across multiple silos, a factory will find each system speaks its own language.

Fortunately, the market is aware of this problem. Many solutions are available, such as implementations of consistent and open standards like OPC UA or industrial protocol gateways to allow the extraction of data from a machine using an unfamiliar protocol. For example, with the help of Modbus-to-BACnet industrial protocol gateways, a heating, ventilation, and air conditioning system can obtain Modbus remote terminal unit data through the BACnet protocol.

Challenge: Meaningless data

The next challenge to data quality is unusable data. Equipment-generated data comes in the form of raw data or values. Information technology (IT) or business analysts cannot make use of the data as is, and manual data processing inevitably inhibits real-time response. If OT data is converted into meaningful IT values first, data can then flow in the edge-to-cloud architecture seamlessly and quickly.

OT data is structured as a series of time-related digits, each representing an event that happens to a specific device or sensor at a specific time, for example, the current magnitude of a certain motor every 10 seconds in the past seven days. Contrarily, IT data is database-residing data with rigorous structures and descriptions that must be given a meaning before being applied for various analyses. Of the OT data mentioned previously, only the numbers 7 and 10 are shown, and preprocessing is required to provide the data with complete meanings (dates, seconds, etc.) by adding the missing context. Only then can further analysis be conducted.

In addition, for the sake of control precision, OT equipment often produces a piece of data in intervals of a second or a millisecond. If every piece of raw OT data is transmitted to an IT system, the IT system will be overwhelmed and not able to do anything purposeful. Even worse, sending meaningless data to the cloud not only reduces operating efficiency but also increases data transmission and storage costs.

To tackle these problems, smart Internet of Things devices are used to regulate the frequency of data distribution. In doing so, OT systems can work in alignment with the needs of IT systems, such as uploading data once an hour, or processing data on the OT side first and only uploading it when a bigger deviation is observed. It takes these steps to excel at OT data preparation.

Challenge: Incomplete data

Digital transformation calls for more diverse and real-time data, and consequently, much more OT data to be transmitted. Although OT networks traditionally transmit data to meet control requirements, industrial digital transformation necessitates data transmission for analysis and decision making.

Challenge: Vulnerable data

OT data becomes not trustworthy mostly from cybersecurity issues. In the past, OT systems did not need to be Internet connected and could be protected simply through physical controls, such as limiting access to an operational area or banning the use of USB sticks and personal computers. As industrial digital transformation takes off, Internet access becomes essential.

With increased connectivity, all vulnerabilities are suddenly laid bare to ruthless computer viruses or thrust onto the radar of profiteering hackers, providing channels to invade systems or disrupt operations. With cyberattacks becoming common, data security and cybersecurity are emerging as required items on every digital transformation agenda. To safeguard production capacity and keep production lines safe from data-tampering attempts, companies must pay attention to OT data security.

A misconception among businesses is that mature IT security solutions can be directly replicated in the OT world. In reality, security tools meant for IT environments are not entirely fit for OT system protection. For example, because OT devices do not run the operating systems compatible with antivirus software, installing it on OT systems is out of the question. Further complicating the antivirus situation is the importance of capacity availability in OT environments; the fear that production capacity will be hurt by data packets being wrongfully blocked has kept many machines away from antivirus software solutions.

Another OT data security issue is the fact that many manufacturers have deployed all devices on the same intranet for the sake of connection stability and convenience. However, once ransomware breaks into that environment, it can easily spread throughout the entire system. It is thus recommended to secure OT environments in three incremental stages: endpoint security, cybersecurity, and security management.

To enhance OT data security capability, industrial firms should:

• Apply intrusion protection system (IPS) technology to OT automation devices to secure critical infrastructure. An industrial-grade IPS monitors data flowing in and out of critical devices, segregates malicious traffic, and notifies administrators the instant an anomaly is detected.
• Take advantage of network layering to curb ransomware attacks. Firms will benefit from upgrading their Ethernet switches to managed Ethernet switches and activating the layering feature to divide an OT network into segments.
• Use network management software to overcome the interoperability hurdles among various OT communication protocols to effectively spot faulty or risky devices via visualization.

Use the four OT data capabilities

As the old saying goes, do not put the cart before the horse. It is critical to get your priorities straight in industrial digital transformation. Do not let poor-quality raw data undermine the results of your big data analyses.

Before trying to source OT data, consider where you are in terms of data acquisition, data preparation, data transmission, and data security. Armed with these four capabilities, you will be able to tackle the challenges head on and leverage high-quality OT data to lay a solid foundation for transformation.

This feature was originally published in the December 2021 issue of InTech magazine.