Inside:
SEPTEMBER 2020
IIoT, it takes a village p5
Cover image courtesy: Trendminer
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IIOT & SCADA
Turn SCADA data into greater profitability On-demand subscription-based predictive process analytics turn SCADA data into a financial opportunity
By Edwin van Dijk
S
upervisory control and data acquisition (SCADA) systems excel when it comes to collecting and storing data and monitoring automated machine performance. Traditionally they were supported by condition-based maintenance tools and historian technologies. That is, until modern, more economical predictive process analytics and the use of IIoT brought improved efficiencies. Predictive process analytics, when applied to SCADA data via IIoT, can deliver an untapped gold mine of efficiencies for a plant’s asset performance. For instance, SCADA data can monitor asset health and when predictive algorithms are applied, it can optimize asset health, and produce substantial cost savings. IIoT has changed SCADA. Today’s SCADA can acquire data from dispersed SCADA systems, unified in a single system, accessible via web and cloud. Other developments include using web services and IIoT protocols to collect exposed data and control processes in real time. There has also been progress seen with process controllers that apply complex business logic and predictive maintenance algorithms to operational data and assets. Just last year McKinsey and Company reported 3 to 5% loss in the www.controleng.com/IIoT
industry overall for equipment effectiveness due to unplanned maintenance. For one chemical processing plant, McKinsey and Co. noted that through the use of analytics, the plant could cut at least half the time it took to repair pumps, which amounted to about $120,000 in costs avoided per pump failure. Predictive maintenance requires the processing of enormous amounts of data and the running of intelligent algorithms, which is costly to apply within local SCADA implementations. It also requires scarce data scientists to be involved to apply those complex algorithms and data models. On the other hand, IIoT platforms can store terabytes of data and can provide global access to data across production plants. These platforms are also capable of combining brownfield sensor data with greenfield sensors and device data, opening new use cases for operational improvement.
The Impact of COVID-19 In striving for more stable operating conditions, many process manufacturing companies today have come to understand the value of data. Unlocking the data from the SCADA system is a hurdle difficult to overcome. Due to the COVID-19 pandemic, investments made in IIoT have decreased and market recovery may take some time. In the mean time, leveraging existing data has proven an agile and profitable way to quickly adapt a business to changing circumstance. At each level of the computer integrated manufacturing (CIM) pyramid (See Figure 1), complex algorithms and data models can be applied to get more value out of the data. But this has proven to be inert, time consuming, cost intensive and requiring scarce resources. What is needed are new tools and technologies such as self-service industrial analytics, where operational experts (such as
FIGURE 1: Computer Integrated Manufacturing Pyramid. All figures courtesy: Trendminer IIoT For Engineers
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process and asset experts), control room personnel and maintenance engineers can analyze the production data themselves. Self-service industrial analytics tools are based on pattern recognition technologies, combined with machine learning techniques, and leverage data science techniques and algorithms in the background. This brings the power of data science into the hands of the process experts, without them needing to become data scientists themselves. With self-service industrial analytics, operational experts can search the process data to see what has happened, how often it may have happened, and what potential root causes may have led to it. Modern self-service industrial analytics tools also provide machine learning techniques to recommend to the user potential root causes to explore. These next-generation solutions are developed to take advantage of IIoT opportunities with ease of use, affordability and scalability in mind.
Case in point Chemicals manufacturer Arlanxeo, Maastricht, Netherlands understood
the value of leveraging time-series data. To start, the company worked with different types of analytics models and identified their limitations for scaling up beyond pilot projects. Over time, they developed their deep knowledge of process operations to create “pattern search-based discovery and predictive-style process analytics” for the average user. The unique, multi-dimensional search capabilities of their platform enable users to find precise information quickly and easily, without expensive modeling projects or data scientists. Using a song recognition app like Shazam or SoundHound as an analogy, the application uses pattern recognition rather than mapping every note in the song to its song database. The pattern recognition software seeks “high energy content,” or the most unique features of a song, then matches that to similar patterns in its database to recognize it. Such pattern recognition works with a high rate of accuracy and speed. Of course, analytics for manufacturing plants require more sophisticated algorithms that go beyond mere search capabilities. Self-service analytics work by connecting to exist-
FIGURE 2. Combining live data with historical context shortens the analysis latency, allowing real-time monitoring.
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ing time-series databases, indexing the data rather than copying it in another system of record. The indexed data makes it easy to find, filter, overlay and compare interesting time periods, to search through batches or continuous processes to pinpoint areas for improvement. Another capability is search for particular operating regimes, process drifts, operator actions, process instabilities or oscillations. By combining these advanced search patterns valuable information that Arlanxeo needs is unlocked.
Process data contextualization Process data contextualization and predictive analytics capabilities add other dimensions and optimize process control systems, making them multi-dimensional for better process quality control. By allowing operations personnel to provide annotation, greater insight is gained. Predictive analytics enable early warning detection of any anomalies or undesirable process events by comparing saved historical patterns with live process data. Moreover, the solution calculates all possible trajectories of the process. Process variables can be predicted before anything happens. Recent process changes can then be matched against expected process behavior and settings pro-actively adjusted accordingly. Forward thinking manufacturers are gaining an incredible competitive advantage using this digital business model and leveraging cost-effective plug-and-play analytics that were developed from the ground up to integrate with digital technologies. IIoT Edwin van Dijk is a company vice president with TrendMiner, ww.trendminer.com. www.controleng.com/IIoT
PREDICTIVE MAINTENANCE
End users, OEMs and technology partners engage on IIoT IIoT-enabled predictive maintenance maximizes uptime, with machinery end users and OEMs working together to determine best practices
By Silvia Gonzale
T
he industrial internet of things (IIoT) solutions and methods enable collection of machine data and monitoring of machine performance and reliability. Both end users and OEMs can act on this data to achieve their goals, improving asset uptime through predictive maintenance and asset efficiency through production analytics. Because of the benefits, digital transformation and incorporation of IIoT concepts have become business priorities, requiring more collaboration than ever to ensure success. This is because digital transformation isn’t just a one-time event but instead a journey involving both technology and people. To ensure they are travelling down the same path, manufacturing plant end users, OEMs, and IIoT technology suppliers are partnering in the design and implementation of equipment to ensure value can be realized, while overcoming the perceived risks of sharing data.
The playing field For greenfield projects, end users will likely expect OEMs to deliver equipment automated with the latest IIoT-enabled technologies. However, many industries have substantial investments in legacy equipment and control platforms, and they also www.controleng.com/IIoT
look for ways to implement IIoT and achieve the associated asset monitoring benefits, with minimal disruption to their operations. Both end users and OEMs share a common desire to determine and implement the best IIoT practices. The IIoT objective cannot be well defined, let alone be achieved, if these parties remain siloed. End users and OEMs are finding they can coordinate with each other, and with IIoT technology partners, to ensure their strategies result in a complete and compatible ecosystem solution. Some design goals are desired by both parties. For instance, automation systems that include technologies that make integration and data sharing easier are preferred because they provide flexibility and scalability. Similarly, designs that are as close to plug-andplay as possible facilitate integration and reduce downtime during retrofits. End users and OEMs alike want to keep the design focus on implementing technologies that add value and begin by addressing the biggest pain points. Because OEMs are designing and building the equipment, it is vital for them to understand end users and how IIoT technologies can help deliver value.
End user concerns Industrial end users want to implement IIoT solutions able to improve
FIGURE 1: By providing ready access to the right IIoT-sourced data, OEMs can help users optimize operations and perform predictive maintenance. All figures courtesy: Emerson
operational and business performance. End users may have multiple sites, and likely operate several different systems at each, so interoperability, reliability, security and scalability are all crucial. While end users will not be able to reach all of their IIoT goals immediately, they definitely want OEMs to enable their machines to collect, store, and analyze the types of data needed for optimizing performance and accomplishing predictive maintenance (See Figure 1). Some end users need their OEM suppliers to adhere to programming, design, and operational standards, a challenge for OEMs delivering systems to numerous different clients. However, adherence to industry standards IIoT For Engineers
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OEMs can reconcile these issues by implementing platforms providing the deterministic control they need and supporting industrial OT as well as IT-friendly communication protocols. This ensures the automation platform can interact with any type of field device, while also exposing the resulting data to end users.
Edge computing solutions
FIGURE 2: OEMs are experts on their equipment and understanding the important “little data” which is available for supporting “big data” analytics leading to optimized operations and improved maintenance.
and industrial communication protocols can lead to improved consistency and compatibility. Digital transformation may not change the underlying equipment operation directly, at least not initially. While users want full access to all the data liberated by IIoT, they may have concerns about sharing this data with other parties, especially via the internet. Data sharing, especially with OEMs hungry to understand equipment performance in the field, may be acceptable to end users if mutual value is realized, such as improving asset reliability. End users usually know the key performance indicators (KPIs) they want to see, but they may need help identifying what other data to integrate via IIoT, especially with regards to establishing valuable predictive maintenance capabilities. Finally, to realize the full benefits of IIoT, users must converge their operational technology (OT) equipment with their IT infrastructure.
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OEM focus OEMs are experts in designing and fine-tuning their machines. Some may be laser-focused on this goal and may therefore be challenged to fully understand how end users need to operate a collection of machines as a system. Nonetheless, every OEM will know the important “little data” available from their machines, which is needed to develop analytics such as overall equipment effectiveness (OEE) indications (See Figure 2). Because OEMs typically produce large numbers of machines, and sometimes many models with a variety of options, they are naturally interested in establishing a standard automation platform for consistency and efficiency within their organization. A modularized automation hardware design and programming approach are usually a good fit for OEMs, but their internal standards may not fully align with those of their end user customers
Edge computing options offered by industrial automation technology suppliers enable end users and OEMs alike to take advantage of IIoT to improve their operations and equipment. Edge gateways and edge devices added to new and existing OT systems collect and transmit data to on-site or cloud-based systems. Making this data available is the first step toward optimizing operations and providing predictive maintenance. An even more comprehensive option is to use edge controllers when automating new equipment. Edge controllers perform deterministic control just like traditional industrial options, but they also integrate IT-friendly general-purpose computing. Therefore, edge controllers are an important way for end users and OEMs to achieve robust and reliable control, combined with secure and seamless integration of OT data with IT systems.
Concluding thoughts Using the right edge computing platforms, OEMs can offer equipment that delivers the KPI, OEE, and predictive maintenance information that end users need to realize maximum value from their capital equipment. IIoT
Silvia Gonzalez is a solutions development leader for Emerson’s machine automation solutions business. www.controleng.com/IIoT
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