Iiot 18 03

MARCH 2018

Inside: ERP embraces IIoT p3 Time-sensitive networking p7 Analog meters under attack p10

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MARCH 2018

COVER DESIGN by Michael Smith, CFE Media.

EDITORIAL

2 Artificial intelligence resets the industrial enterprise

Transactional, GIS, and turbine data, resident in a single database

FEATURES

3 Enterprise resources

planning looks to embrace the Internet of Things ‘Servitization’ of product lines is one sought-for goal

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7 With control at the edge, ERP becomes the potential aggregation point for all data,

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bypassing traditional control systems.

7 Time-sensitive networking

solves challenges in measurement and control Entire structures are being outfitted; sensors measure strain, pressure, load, and torque

10 Are analog meters to blame?

Cybersecurity concerns influence nuclear industry automation

10

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Companies can build on the power of accumulated data. Manufacturers do have

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deep troves of simple data that convert to use cases.

www.controleng.com/IIoT

IIoT For Engineers

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PRODUCTIVITY AND BEST PRACTICES: EDITOR’S COLUMN Kevin Parker Senior Contributing Editor

Artificial intelligence resets the industrial enterprise Transactional, GIS, and turbine data resident in a single database

A

have deep troves of simple data which can be converted utomation can improve a process. Productivity can gain from examination of workflows and lead- to use cases, where they can go deep.”

Asked to compare the potential impact of today’s ing indicators. And analytics deliver insight as to how things are going. But, it isn’t till you step over into the emerging technologies with those of the 1980s, when cognitive, with things like machine learning and semantics, PLCs, DCSs, SCADA, CAD, and ERP were all introduced, Kavanaugh said, “The introduction of new technolothat the realm of artificial intelligence (AI) is entered. gies of the 1980s brought significant change, but it was For the Industrial Internet of Things (IIoT), predictive basically the automation of rows and columns, applied maintenance of machinery and equipment is the first to the plant floor and out in the field. Today, incorporatapplication demonstrating wide commercial acceptance. ing experience, a multi-attribute perspective of what “This can be done with classic regression and predictive actually happens, is a bigger part. We’re talking about analytics. With artificial intelligence, however, you go bethings that are inherently cognitive, in yond the structured deterministic to the other words fuzzy. While the earlier fuzzier stochastic,” said Jeff Kavanaugh, transformation was from full analog to vice president, senior partner, Infosys.

People have more computerized operations, the current “With machine learning based on input one is more pervasive, more connectsuch as audio signatures, the computer than they think, but ed, more intelligent—and ultimately— learns as a human would, by first paymore profound.” ing attention to how a machine sounds less than they hope when it’s healthy and then understanding anomalies.”

AI as enterprising (data that is).

Infosys recently conducted a global Many readers of CFE Media engineersurvey on the adoption of intelligent ing titles are looking for AI on plant automation. The survey’s central point, floors. As a feature article in this issue that AI technology is going mainstream, is a good one. by veteran technology journalist Sidney Hill Jr. suggests, At the same time, for example, while AI adoption rates it’s in the enterprise as well. In fact, with control at the are shown to vary by region, more analysis is needed to edge, ERP becomes the potential aggregation point for all determine root cause. data, bypassing traditional automation control systems.

The impact could be profound.

For example, as an in-memory database ERP system, Sample set asymmetry A question often asked is whether companies have the SAP’s HANA was ahead of its time. Its latest advance is the introduction of a geographic information systems data needed to enable machine learning, and whether (GIS) capability, but not just as an application feature. the data is in a form suitable for such use. “People have Integration extends the capabilities into a standalone more data than they think, but less than they hope,” product. One database runs the business applications said Kavanaugh. “While there are a lot of data stores and the GIS. In a case example, one company already that don’t lend themselves to machine learning, there combines transactional data from SAP ERP central comare instances where great amounts of data simply aren’t ponent with geospatial data and other data taken from needed. At other times, companies can build on the power of accumulated data. Industrial manufacturers do turbines. IIoT

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IIoT For Engineers

www.controleng.com/IIoT

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IIoT IN THE ENTERPRISE

Enterprise resources planning looks to embrace the Internet of Things ‘Servitization’ of product lines one sought-for goal

By Sidney Hill Jr.

I

t’s easy to see why manufacturers like the concept of the Industrial Internet of Things (IIoT). It represents something most of them have been seeking for decades—an end-to-end information network that provides real-time visibility into all their operations.

That possibility is the likely reason manufacturers are expected to have spent the biggest share of the $800 billion that International Data Corp. (IDC), an industry analyst firm, expected companies to invest in Internet of Things (IoT) technology throughout 2017.

That figure, which includes all industries and all forms of technology spending—hardware, software, services, and connectivity—represents a near 17% increase over 2016. IDC expects that trend to continue with overall IoT spending exceeding $1.4 trillion by 2021.

Manufacturers will lead the way, with estimated spending of $183 billion in 2017 alone, according to IDC. Most of the money manufacturers spent on IIoT in 2017 was earmarked for projects designed to improve production operations. That points to one piece of good news and one piece of bad news about the state of IIoT in the manufacturing sector. www.controleng.com/IIoT

The good news is IIoT technology can have a major positive impact on production operations. There are, in fact, already many instances of manufacturers revamping business processes in ways that reduce costs, or

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Only a small percentage of companies that have deployed IoT technologies have yet to figure out how to move IoT data to their

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ERP systems.

boost revenues, using IIoT technology.

Some have leveraged the technology to create entire new business models and generate new revenue streams.

The bad news is IIoT technology is not mature enough to help most manufacturers take the final step of creating networks that can convert production-related data into information that can be viewed and analyzed in enterprise resources planning (ERP) and other enterprise systems. Such networks, which would allow manufacturers to anticipate and respond to potential problems before they cause business disruptions, are the true

promise of IIoT. Manufacturers are getting a glimpse of this promise as they incorporate this technology into their operations.

Service as a business model One example is STILL, which makes and sells warehouse material handling products primarily in Europe and Latin America. STILL recently equipped forklifts with sensors that receive and respond to instructions on when and where to move material around a warehouse. Because those instructions are delivered by a web-based warehouse management system, STILL believes it will be able to change the way it bills customers for using its forklifts.

Instead of selling a customer a set of forklifts and walking away, STILL envisions offering a forklift service such that invoices are based on the amount of material moved over a billing cycle. The combination of IIoTenabled sensors and the web-based warehouse management system lets STILL track volume remotely and issue correct invoices.

Kaeser Compressors has harnessed IIoT technology in a similar fashion to launch a new business called Sigma Air Utility. Under this model, Kaeser installs compressors in a customer’s facility and uses its web-based network IIoT For Engineers

MARCH 2018

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IIoT IN THE ENTERPRISE

to track how much air the facility uses. It then bills the customer only for the air used; just as electric or gas utilities bill customers for consuming energy.

These business models have inspired the industry buzzword “servitization,” which refers to using a company’s product as the basis for a business built around a recurring, service-based revenue stream. These models are popular among manufacturers because profit margins on services have proven to be higher—sometimes as much as 20%—than on products.

These models also bring manufacturers closer to the true end-to-end networks that IIoT promises because they require connecting IIoT-enabled devices with at least one or more enterprise-level application. For instance, STILL and Kaeser monitor the equipment deployed in their servicebased businesses through modules of an ERP suite.

“Companies like STILL and Kaeser are well along the maturity curve in showing how IIoT can transform

business models,” said David Parrish, SAP’s senior global marketing director for the automotive and industrial sectors. “They also are exceptions. Most of the companies we work with are just starting that journey.”

Parrish and other industry experts noted that forces are aligning to make it easier for manufacturers to leverage IIoT technology in their individual businesses.

The IIoT maturity curve “A lot of companies have been thinking about IIoT for a while,” Parrish said. “But a few things have happened over the last few years to stir more action. Sensor prices have come down. The ability to use technology to analyze large volumes of sensor data—the whole Big Data thing—is becoming more real. The result is everyone is doing something. How much they’re doing now is largely dependent on the sophistication of the IT group or how driven the operations people are about staying innovative.”

Enterprise resources planning (ERP) in the modern era ERP IS A SUITE of applications for the integrated management of core business processes, from manufacturing to distribution, finance, and human resources, using a common process and data model.

Aberdeen Group positions ERP as follows: ERP is the single most implemented enterprise application. ERP lays the foundations for standard business processes.

ERP is a living system that is maintained, extended, and evolved after initial implementation.

Best-in-class manufacturers are much more likely to have a multiERP or federated strategy than average or laggard organizations.

As the information technology infrastructure found in manufacturing enterprises evolves, ERP is likewise changing, with cloud modalities having the biggest impact.

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IIoT For Engineers

A recent survey commissioned by IFS, another ERP software provider, supports the notion that only a select few manufacturers are very far along the IIoT maturity curve. The survey collected responses from 200 manufacturing professionals with authority to make IIoT technology purchasing decisions in their respective companies. The responses to one question in particular spoke to manufacturers’ IIoT maturity level.

The multiple-choice question asked how IIoT data was consumed across the enterprise and offered the following list of possible responses: • Equipment-mounted diagnostic tool used by plant floor technicians • Computerized maintenance management system (CMMS) used by maintenance staff • Supervisory control and data acquisition (SCADA) system used by plant floor management • Process automation system used by plant floor management • Manufacturing execution software used by plant floor management • Enterprise asset management software used by plant managers • Asset performance management software used by plant managers and senior executives • ERP software used by senior executives.

Only 16% percent of respondents said IIoT data is available to senior management through an ERP system.

This confirms that even companies that have deployed IIoT technology still struggle to take that final step toward building an end-to-end information network. That doesn’t mean manufacturers aren’t getting a return on their IoT investments. www.controleng.com/IIoT

Quick article synopsis

A logical progression Rick Veague, IFS’ chief technology officer (CTO) for North America, said automating production processes and creating condition-based equipment maintenance programs were the most common ways in which survey participants said they use IIoT technology. Lower operating costs typically are the biggest benefit from using IIoT technology in this fashion.

Veague said it’s logical for manufacturers to choose these types of projects for their initial forays into the IIoT realm. Veague noted manufacturers have been attaching sensors to plant floor machines and equipment for decades. It’s not difficult to connect those sensors with software applications—like industrial automation and maintenance management systems—that also operate on the factory floor. While the cost savings from this approach are real, and sometimes substantial, Veague believes manufacturers can reap even greater value by extending their IIoT footprint beyond the factory floor.

“Right now, the primary value industrial companies are seeking from IIoT seems to be cost avoidance,” Veague said. “The greater potential for IIoT in these settings, however, is business growth. To realize that benefit, companies must not only think creatively about IIoT. They must be able to use IIoT data in the context of their business, which means tighter integration with applications like ERP.” Veague also said manufacturers are not necessarily at fault for not pushing more IIoT data to their ERP systems.

Until recently, ERP systems simply were not able to consume such data, and the responses to another question in the IFS survey indicates that remains a problem for many manufacturers. Roughly 49% of the respondents said their ERP www.controleng.com/IIoT

system performed “somewhat well” at consuming IoT data, with another 6% saying their systems performed that task extremely well.

The vendor perspective “It’s interesting that not even the most advanced companies were likely to say their enterprise software did a very good job of helping consume IIoT data,” Veague said. “This indicates the level of maturity of enterprise software. There obviously is room for growth here. . .the ability of ERP and other software applications to support IIoT still is not robust enough.”

The ERP vendor community is aware of this fact, and almost every vendor in the industry has an IIoT strategy. Some ERP vendors are further along than others when it comes to IIoT-enabling their platforms, but there is one thing all seem to agree on: a truly IIoT-enabled ERP system must be cloud-based.

That’s why even the industry giants such as SAP and Oracle, with their huge customer bases that are still running on-premise versions of their software, are now touting the virtues of software in the cloud. In fact, both Oracle and SAP have been working somewhat quietly over the past several years to build a portfolio of products that can support end-toend, IIoT-enabled business networks.

For instance, SAP has purchased companies like Ariba, Concur, and Success Factors, which gives the company instant access to cloudbased systems for procurement, time, and expense reporting and human resources management. SAP is also developing a cloud-based version of its Manufacturing Integration and Intelligence product.

That’s where IoT data taken from the shop floor would stop first

PROBLEM: Manufacturing companies are constantly looking for better ways of integrating data from all their operations. They are particularly interested in moving shop-floor data to the enterprise systems, in hopes of helping managers and executives make better, more strategic business decisions.

SOLUTION: A properly built IIoT infrastructure can facilitate the flow and aggregation of data across the enterprise to give users a constant, real-time view of all processes. Such a network can help a manufacturing company in many ways, from reducing costs, helping to spawn new lines of business, and even in recruiting top-level talent.

ACTION TO TAKE: Talk with suppliers of both process automation and ERP solutions to determine their strategies for facilitating the use of IoT across an enterprise. Examine which parts of your business can benefit from use of IoT first, develop pilot programs in conjunction with an enterprise-wide vision.

before being presented to higherlevel ERP applications.

“Manufacturing Integration and Intelligence is now the lifeblood of our manufacturing solutions,” Parrish said. “It’s being designed for the cloud and for data integration and transfer from a communications standpoint.”

Terri Hiskey, vice president of product marketing for Epicor, said manufacturers seeking to build an IIoT infrastructure need a cloud-based ERP system because that’s the only type of platform that can facilitate the connections that are needed to turn all the information coming from various shop floor devices into useful information.

“You need to look at the system’s architecture,” she said. “It needs to IIoT For Engineers

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DURABLE AND RELIABLE

IIoT IN THE ENTERPRISE

be built on open standards with open application programming interfaces. The newer, cloud-based systems are being written in that fashion; the older, on-premise systems may not have been.”

David Gustovich, vice president with Oracle’s NetSuite Global Business Unit, said the ultimate purpose of feeding IIoT data to an ERP system is giving users information that can be viewed and analyzed to make better business decisions. He agreed that building an infrastructure on open standards is essential to making that happen, but he also believes the focus should begin at the device level.

“When thinking about the Manufacturers IIoT, you must consider how appear split almost you’re going to make your machines and devices talk a evenly in their opincommon language that can be brought into the strucions about how well tured data environment of an ERP system,” Gustovich said. their ERP systems “Many companies will have consume IoT data. to invest in new programmable logic controllers (PLCs), control panels, and drivers that are OPC-compliant. OPC becomes the standardized communication protocol to amalgamate all these different data tags from the different types of devices and equipment.

When you aggregate that data, it can be passed into the cloud in a more efficient and secure manner.”

Hiskey said mastering the art of aggregating and presenting IIoT data through an ERP interface will provide manufacturers multiple competitive advantages going forward. “It important to think about how IIoT can impact the user experience,” she said. “We’ve all being spoiled by the Amazon experience—the 1-click, easy-to-use interface.

That’s bleeding into the business-to-business arena. As more millennials come into the workforce, especially in the old-line industries, you’ll need those types of interfaces to fill your jobs.

“It’s on us as software developers to think about the experience we’re offering to both the employees of manufacturing companies and their customers,” she said. “The manner in which we connect IIoT technologies with ERP software will determine the quality of that experience.” IIoT

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E-NEWS

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Sidney Hill Jr. is a graduate from the Medill School of Journalism at Northwestern University. He has been writing about the convergence of business and technology for more than 20 years.

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IIoT For Engineers

NETWORKING AND CONTROL

Time-sensitive networking solves challenges in measurement and control Entire structures are being IIoT-outfitted; sensors measure strain, pressure, load, and torque

By Brandon Treece

O

f the 50 billion devices it is said will be connected by 2020, experts estimate that the industrial sector will account for nearly half. This means that engineers and scientists have a big voice when it comes to implementing the Industrial Internet of Things (IIoT) across factories, test laboratories, power grids, refineries, and infrastructure.

Engineers can expect to gain three key benefits: • Increased uptime with predictive maintenance • Boosted performance with control at the edge • Improved product design and manufacturing through connected, real-world data Today, the uptime of the most critical assets often depends on manual inspection by a few subject matter experts. Unfortunately, these experts are becoming harder to find, and manual monitoring is not scalable across an entire fleet of assets. It’s estimated that only 5% of www.controleng.com/IIoT

data collected today is analyzed. IIoT helps illuminate the possibilities for using analytics and machine learning to predict the amount of remaining serviceable life of an asset and to schedule maintenance before a costly failure.

The second benefit involves increased and optimized performance.

Smart machines deployed on production lines or in the field must be able to adjust parameters, such as temperatures or throughput, depending on information they receive from other machines upstream. The best way to boost performance for these systems involves full autonomy on the part of the machine or asset. In this case, if machines can learn, either from other machines or through their own “experiences,” they can then tune

Time Synchronization

control parameters and better adapt to their surroundings.

The final benefit of IIoT is improved product design and production. This is sometimes referred to as “IIoT for R&D.” Real-world data, such as usage data, is fed back into engineering to improve the next product generation. However, it’s not just about acquiring the data but also effectively managing it to gain valuable insights. The Jaguar Land Rover Co., for example, has hundreds of engineers who collectively generate more than 500 gigabytes of time-seriesdata per day. Previously, the company analyzed only 10% of its data, but after implementing an IIoT solution, it increased that coverage to 95%. It can now address more design issues in a lesser amount of time.

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IIoT For Engineers

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NETWORKING AND CONTROL

Realization forthcoming To realize the benefits of IIoT, design teams must depend on several core technologies. Regardless of whether it’s building an online monitoring system, smart manufacturing machine, or testing a physical electromechanical system, a key commonality is the need for intelligence at the edge. The more sophisticated systems get, the more decisions need to be made in real time. For example, the ability to acquire massive amounts of high-resolution analog waveform data for the structural test of a wind turbine blade is important to understanding the characteristics of its real-world behavior. At the same time, that data needs to be processed to provide input to the control system that is actuating the blade to ensure the test is conducted under known conditions. So, it should be no surprise that experts estimate that at least 40% of all IIoT-created data will be stored, processed, analyzed, and acted on at the edge, according to IDC. To maximize performance and reduce unnecessary data transfers, you must push decision making down to edge nodes deployed at or near the

“things.” In implementing these systems, new challenges appear—especially as systems grow in physical size and sensor count. Continuing with our structural test example, to get a full understanding of how a wind turbine blade will perform, the entire structure needs to be outfitted with sensors to measure strain, pressure, load, and torque. These sensors all produce analog signals, and to gain the most insight, high-speed, high-resolution measurements must be taken. For large-scale applications such as this, hundreds or even thousands of sensors might need to be distributed throughout the system. While we acquire all this data, it must be processed in real time in order to provide the set points for all the actuators that control the system.

A few challenges arise when attempting to develop such a system: • Synchronizing the potentially thousands of channels and numerous measurement systems • Synchronizing the control systems such that all actuation happens at the correct time

• Synchronizing the measurements and control systems.

These challenges are further exaggerated as systems grow, and more measurement and control capabilities are added to an application. Synchronizing measurement systems with other measurement systems, and control systems with other control systems, is not a new challenge. Typically, this can be achieved with signal-based methods, in which physical cabling is used to route a common time base or signal to distributed nodes. Unfortunately, this has limitations on distance and scalability, as well as a risk of noise.

Another possibility Another option is to leverage a protocol built on top of a common standard like Ethernet. Ethernet offers openness and interoperability but has no bounds for latency or guarantees for bandwidth. To solve this challenge, custom versions of Ethernet, often referred to as hard real-time Ethernet, have been developed. EtherCAT, Profinet, and EtherNet/IP are prime examples. These custom variants of Ethernet provide hard real-time

An important aspect of time-sensitive networking is the convergence of time-critical traffic and other Ethernet traffic.

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www.controleng.com/IIoT

performance and best-in-class latency and control. However, each variant includes both hardware and software modifications to network infrastructure, which increases costs and means different devices from different vendors cannot function on the same network.

A new technology to solve this synchronization challenge is coming to market now, called time-sensitive networking (TSN). TSN is an evolution of standard Ethernet that provides openness and interoperability but adds the bounded latency and guaranteed bandwidth provided by hard real-time Ethernet. Specifically, TSN delivers three key components: time-based synchronization, traffic scheduling, and system configuration. The synchronization capabilities are based on the IEEE 1588 precision time protocol profile, providing a sub-microsecond level of network synchronization. In addition, traffic scheduling and system configuration power deterministic data communication, so users can schedule and prioritize time-critical data (such as control signals) across the network.

An important aspect of TSN is the convergence of time-critical traffic and other Ethernet traffic. Because TSN is a feature of the Ethernet standard, the new capabilities of time synchronization and deterministic communication run over the same network as other network communication. This means a single port on the measurement or control system can perform deterministic communication while also updating remote user interface terminals and supporting file transfer.

TSN is a key enhancement for many industrial applications, such as process and machine control, in which low communication latency and minimal jitter are critical to www.controleng.com/IIoT

meeting closed-loop control requirements. Time-based synchronization over Ethernet also minimizes cabling traditionally found in monitoring applications and physical system testing (like the structural example) leading to a simpler, cost-effective solution without sacrificing reliability.

‘

TSN is an evolution of standard Ethernet for openness and interoper-

ability but adds bounded latency and guaran-

’

teed bandwidth.

The introduction of TSN is a huge step forward in solving the whole system synchronization challenge.

Another concern engineers developing these systems have is reducing the overall system complexity while maintaining or improving reliability. In our structural test example, the application can be developed to leverage TSN-based measurement and control systems, but there is still the challenge of programming the two in their respective environments and dealing with their respective data collection mechanisms.

Removing the challenge Programmable logic controllers (PLCs) are used in IEC 61131-3 languages and operate on single-point data. This type of data is ideal for control applications, but not suited for gaining insight—waveform data is needed for this. Similarly, measurement systems work in waveform data

to deliver the needed insight but are not ideal for sending single-point control signals or reacting to them deterministically.

There has been a slow convergence of measurement and control systems over the past several years. New capabilities have been added to each, such that more measurement systems can do some control, and vice versa. This continued convergence of measurement and control systems will have a major impact on IIoT applications in the future. Measurement and control systems will become synonymous, eliminating the need to deploy both systems. Measurement and control will be able to be done using a single software toolchain to acquire, process, log, and react to incoming data.

Having separate measurement and control systems makes systems more complex since two independent systems must be created, often using disparate software tools. Then those systems must be integrated together, which results in additional cabling and related considerations.

Instead, a fully consolidated measurement and control platform should be considered. Such a platform would need to offer the measurement breadth, accuracy, channel scalability, and I/O synchronization needs of a measurement system as well as the customizability of a control system.

By leveraging such a platform, test engineers, maintenance managers, and machine designers alike will have the necessary tools at their disposal to fully realize the benefits of the IIoT, while reducing the cost and complexity of their deployment. IIoT Brandon Treece is a senior product marketing manager, National Instruments, Austin, Texas.

IIoT For Engineers

MARCH 2018

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THE ANALOG AND THE DIGITAL

Are analog meters to blame?

Cybersecurity concerns influence nuclear industry automation

By Otto P. Fest

M

ore than 120 years ago, Sir Edward Weston invented the analog panel meter (APM). The meter’s simplicity, along with its low manufacturing cost, is unfortunately offset by its unreliability, inaccuracy, and fragility.

Moreover, its readings are subject to misinterpretation.

Unfortunately, the meter’s propensities toward stuck needles, inaccuracies, and erroneous readings have, upon occasion, been the cause of tragedy.

This inherent inaccuracy caused the military and nuclear power industry to adopt a precautionary ~5% to 10% safety factor on all readings. Most APMs have an accuracy of only +/-5% (+/-2%, if you are looking at them straight-on from a distance of +/-10 in.), plus parallax, when interpreting the meter. As a result of this seemingly minor discrepancy, a typical nuclear power plant could lose more than $100,000 of saleable power daily.

The industry cannot remain dependent on late 19th-century metering technology. That is why we at Otek Corp. were intrigued when challenged by a nuclear plant manager to develop an electronic bar-digital meter identical to our New Technology Meter (NTM), but without a microprocessor.

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As a signal powered meter, the NTM is powered by the current loop (4 to 20 or 10 to 50 mA) or by the ac/dc signal that it measures.

The resulting solid-state analog meter (SSAM) has no microprocessor or control outputs. The SSAM is for display only, while the NTM is for display or control. Both series can be powered externally and feature the company’s signal-failure alarm that sounds for about 30 seconds after failure, eliminating the dangerous “stuck needle” syndrome.

A longtime industrial staple, analog meters are being replaced in production environments. All images courtesy: OTEKCorp.com

Cybersecurity mandates Nuclear power plant instrument-and-control (I&C) rooms must comply with Nuclear Regulatory Commission (NRC) cybersecurity regulations (including NEI08-09), if they go digital. Doing so costs millions of dollars and implementation can take years. Otherwise, if industry managers keep their +/-5% analog meters, they must operate at below optimum efficiency, in an extreme case losing 8% to 10% of their optimum electric-power output.

At Otek, we asked ourselves, “How in the world can we deliver the needed performance without a microprocessor?” Fortunately, more

than a decade previously, we had experimented with a hardware-only bar-graph digital meter. Within two weeks, after a frantic search for a 2004 notebook and the prototype breadboard, we had a multicolor bar-digital meter solely powered by the 4 to 20 mA (~5 to 60 mW) current loop without a microprocessor.

Although we hadn’t replaced Sir Weston’s invention, it met the plant manager’s challenge and now is being evaluated for Class 1E classification. However, further work engendered a new breed of LED electronic meter that measures, displays, controls, and transmits ac and dc signals without a microprocessor, and all with 0.1% digital accuracy, versus 5% for analog meters.

The microprocessor-less and powerless SSAM series can be used as a basic bar graph (like the analog meter), as a bar-digital (like obsolete digitals), or as a multi-function meter that is field-configurable. If the housing is the same, all that’s needed is to change the scale plate and pluggable modules and perform any required calibrations.

Addressing a legacy At a demonstration of powerless technology at a nuclear-industry corporate “lunch and learn” session, another manager asked: “How should the I&C manager decide whether to digitize the I&C room?”

The points to consider are as follows: Use of the current loop is the simplest, most reliable means www.controleng.com/IIoT

of transmitting analog signals.

Therefore, the options include: 1) replace only the analog meters; 2) add a data-acquisition system; or, 3) add supervisory control with triple redundancy. However, both 2) and 3) require cybersecurity compliance.

Option 1) does not have to comply with cybersecurity requirements, as there are no microcontrollers or serial I/O to hack.

Specific to 1), analog-meter replacement only, options include: a) change all signals and wiring to 4 to 20 mA; b) install the best 4 to 20 mA transmitters; or, c) install the SSAM or equivalent. With wire-per-wire and panel-compatible drop-in ability, existing wiring and panels can be reused.

A typical reactor produces about $1 million daily of electric power, so every 1% increase in productivity is about $10,000 per day of profit or about $3.65 million per year.

Industry snapshot About 95% of U.S. nuclear power plants were built and commissioned before 1990. For a length of time, all operated safely and at about 90% efficiency. A limited number of governmental regulations allowed profitable operation with about one-third of present personnel and expenses.

Today, the cumulative impact of the 1986 Chernobyl meltdown and 2011 Fukushima disaster on the

Because use of microprocessors presents cybersecurity and regulatory challenges, use of analog meters may persist for some applications and industries, including the nuclear power industry.

regulatory environment, as well as recent cybersecurity mandates, means that most non-regulated plants operate at a loss. Regulated

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THE ANALOG AND THE DIGITAL

ones, guaranteed a profit via subsidies and rate increases, have no incentive to improve efficiency. The result is that six U.S. nuclear power plants recently announced closures with more to come.

Following the Chernobyl disaster, Otek developed its HiQ series, a breed of electronic meters to replace old analog meters and to prevent operations errors. After the Fukushima disaster, Otek introduced its NTM series to replace obsolete analog meters and prevent the stuck-needle syndrome.

Subsequently, the NRC released a cybersecurity directive to protect plants from terrorist attacks. The directive requires the nuclear industry to spend more than $20 million dollars per control room if they implement any critical microprocessor-based digital assets.

Options galore The nuclear industry has six identifiable options when it comes to digitizing I&C rooms: 1. Do nothing, keep the current obsolete analog or digital instruments, and lose roughly $10,000/day, or $3.65 million/year, for every 1% loss in efficiency in an approximately $1 million/day plant.

2. Go for broke with flat screens and triple-redundancy supervisorycontrol, achieving compliance with cybersecurity regulations at an estimated cost of more than $200 million, meaning a seven-year wait for a return to profit.

3. Change to flat screens and a data-acquisition system, which will include cybersecurity compliance, as well as a new I&C room, engineering designs, wiring, training of operators, and NRC approvals. Costs of about $75 million will be incurred, as well as the loss of 1% per day of income.

Return on investment (ROI) will take more than three years.

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4. Replace obsolete meters with the Otek NTM series, or equivalent, which are SCADA/DAS/DCS class 1Ecompatible, instead of flat screens.

This means no new wiring, except serial I/O, no power supply, and no new building costs. This saves about $140 million, but adds cybersecurity compliance costs. ROI will take only about 18 months—assuming a 10% increase in efficiency or about $36.5 million/year.

5. Implement a data-acquisition system with the Otek NTM series, or equivalent. This is similar to option 3), but at a lower cost of about $35 million, and without associated productivity loss or a new building. ROI is about one year for a typical 5% to 8% increase in efficiency, including cybersecurity compliance costs.

6. Replace analogs with SSAM, or equivalent, and operate the plants as usual (manually) until decommissioned, but at higher efficiency and profitability.

Brief summation The need to digitize control rooms for greater safety and profitability is not only crucial for the nuclear industry, but also for the U.S. Navy, the oil & gas industry, the electric power industry, or any other industry using analog meters. For a non-regulated utility dealing with a reactor having a limited operating life expectancy, or in a very competitive market, a good choice is to replace obsolete analog and digital meters all at once or as needed.

We feel we’ve done justice to the long-lived analog meter. Yet all good things come to an end, and it is nearing extinction.

Dr. Otto P. Fest is president and founder of OTEKCorp.com. www.controleng.com/IIoT

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