Pave The Way for Digital Success - Plant Services

Pave The Way for Digital Success

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CONTENTS

How digital greenfield projects can pave the way for brownfield IoT success 3

Overcome obstacles to remote monitoring 7

www.PlantServices.com-2-

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How digital greenfield projects can pave the way for brownfield IoT successThe advances made by assets designed for reliability highlight the brownfield opportunities available

to plant professionals

By Sheila Kennedy, CMRP, contributing editor

Connectivity is king for the industrial internet of things (IIoT). Connectivity of machines, devices, data, and people is the hallmark of digital transforma-tion and of all the opportunities that come with it. While the pace and extent of digitalization varies from plant to plant, the great-est divergence is between new (greenfield) and existing (brown-field) sites.

In a fully optimized plant, everything is smart: the machines, instrumentation and control sys-tems, sensors, motor controls,

robotic systems, condition moni-toring devices, safety systems, and more. Full optimization may be achievable in greenfield projects with adequate capital funding, but legacy sites can only hope for piece-meal deployments.

Digitalized asset monitoring and management is becoming the norm in brand new plants, but it is also a launching point for IIoT in existing and limited-budget environments.

BOOSTING BROWNFIELD

PERFORMANCE

At brownfield sites, IIoT initia-tives are typically associated with upgrade or expansion projects. Digitalization is more challeng-ing in these environments due to aging facilities, equipment, and networking systems (or lack thereof ), as well as data that is

often siloed or paper based and generally of inferior quality. It is, however, a necessary endeavor in order to benefit from IIoT-en-abled operational improvements.

Many older plants are in dire need of asset optimization, making

transitioning from run-to-failure (RTF) and routine preventive main-tenance (PM) practices to condition monitoring and predictive main-tenance (PdM) a prominent IIoT goal. Adequately identifying and correcting deteriorating machine conditions before failure requires open, standards-based communi-cations along with integration and interoperability between various wired and wireless machine sensors, portable or drone-based condition monitoring devices, and enterprise asset and service management software.

Vibration analysis, thermogra-phy, ultrasound, and oil analysis are common PdM approaches. Real-time, threshold-driven alerts prompt the planning and sched-uling of repairs or replacements, and automatic escalation ensures the work is completed in time to avoid costly equipment failure. As a result, PdM helps to reduce maintenance and inventory costs, improve safety and performance, and extend asset longevity.

Many older plants are in dire need of asset optimization.

plaNT SeRViceS: Special RepORT


The newer iteration of PdM is prescriptive maintenance (RxM), which not only predicts failure but also recommends or “prescribes” corrective actions with help from advanced analytics. Assets that would benefit most from fail-ure prediction and prevention, whether for production, safety, or environmental reasons, are prime candidates for PdM and RxM investments in existing plants.

Advanced analytics leverage machine learning (ML), artificial intelligence (AI), and pattern recognition to formulate deci-sions from streaming and static big data, with minimal human intervention. Analytics in the cloud and on edge devices help to form better conclusions about asset management and main-tenance, from predicting or diagnosing failures, to making repair and replacement deci-sions, to proactively applying the actions to similar assets under similar conditions. Over time,

the algorithms become “smarter” and more accurate as the systems learn from experience.

3D modeling and simulation of assets, systems, processes, and complete plants is possible with

digital replicas, or twins. Older plants tend to deploy digital twins on a smaller scale due to incom-patible data sources, structures, and equipment, which complicate setup. The IIoT-enabled capability is useful for simulating operation, monitoring machine health, pre-dicting maintenance, simulating repair scenarios, and forecasting responses to process changes. To remain effective, digital twins must

be continuously updated to match changes made to the physical twin.

Augmented reality (AR), vir-tual reality (VR), mixed reality (MR), and virtual agents provide new and immersive ways to tackle

personnel training, development, and collaboration. Field service and plant maintenance roles can be learned and practiced in safe, realistic environments, and con-ducted live with on-demand access to remote experts for advice or collaboration. The technology is helpful for retraining and reskill-ing personnel for new roles arising from digitalization.

Data quality and accessibility, qualities seldom associated with older plants, are fundamental to effective AI and ML. Since learning from bad data will inevitably produce faulty results, some companies are establish-ing data quality groups focused on cleansing data, making ML a more viable goal. Example objectives include ensuring data relevance, accuracy, complete-ness, and integrity; eliminating

Data quality and accessibility, qualities seldom associated

with older plants, are fundamental to effective AI and ML.

pRiORiTiZiNG iiOT pROJecTS iN eXiSTiNG plaNTS ReQUiReS cONSiDeRaTiON OF aSSeT cRiTicaliTY. eXaMpleS MaY iNclUDe:

• Installing vibration and thermal condition sensors on certain categories of equipment or factory lines

• Implementing infrared thermography to monitor certain equipment and facility hot spots or plant wide

• Employing AR to help guide equipment-facing employees through complex maintenance tasks

• Creating a digital twin of a critical asset or system to facilitate monitoring and maintenance decisions



redundancies; and digitizing knowledge stored in the mem-ories or personal records of seasoned professionals.

Resistance to change is another challenge unique to existing plants. New technologies and processes disrupt established comfort zones, creating fear of failure, fear of being replaced,

and lack of buy-in. Employees who thrive on new technology adapt better, but those who don’t may find digital transformation a threat. Effective organizational change management is imperative to ensure IIoT and digitalization projects succeed.

ENGINEERING GREENFIELD

SITES FOR RELIABILITY

The advantage for new plants is the clean slate from which they are designed, modeled, and engineered for reliability. Industry 4.0 green-field smart factories are digitalized from the ground up. They have an open IIoT architecture that is standards-based, interoperable,

extensible, scalable, and adaptable to new and inevitably improv-ing technologies. They enjoy full democratization of data and are staffed by employees who embrace all things digital.

Greenfield plants uniquely benefit from fresh, clean data; state-of-the-art assets, systems, and processes; integrated smart devices

and sensors; and full connectivity. Some are so robotic and AI-driven that asset management and mon-itoring is performed off-site and maintenance crews are only dis-patched on an as-needed basis.

In brand new plants, it is feasi-ble to create a digital twin of the entire plant and all its equipment, from conception to the as-de-signed, as-built, and progressive as-maintained states. Modeling and simulation of assets and processes using clean, complete, quality data ensures the analytics are more intelligent, the PdM and RxM findings are timelier, the pre-scribed actions are more accurate, and the ML delivers increasing

intelligence and value to the orga-nization. Having a digital twin of the whole plant extends the value, enabling plant-wide continuous improvement.

UNIVERSAL APPEAL OF IIoT

FOR ASSET MANAGEMENT

AND MONITORING

Digitalization of asset management and monitoring, implemented with the latest cybersecurity best prac-tices, offers lucrative rewards. It sets the path for maintenance and field service optimization for plants of any age, and for maintenance and reliability service providers. It also accelerates the journey to a serviti-zation business model for original equipment manufacturers (OEMs).

Greenfield and brownfield asset owners and operators, service pro-viders, and OEMs all benefit from heightened awareness of machine operating and performance trends, ever-smarter analytics, early notification of anomalies, and efficient, cost-effective corrective actions. The resultant improve-ments in uptime, performance, and productivity are evidenced in key performance indicators such as mean time between failure (MTBF), mean time to repair (MTTR), and overall equipment effectiveness (OEE). If you are not already on the IIoT journey, now is the time to start.

The advantage for new plants is the clean slate from which they are designed, modeled, and engineered for reliability.



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Overcome obstacles to remote monitoring Move data directly from the plant floor to the cloud by using this set of IIoT technologies

By Josh Eastburn, Opto 22

New hardware and software systems for remote monitoring and predictive maintenance promise to reduce downtime, human error, and cost by improving early detec-tion and automating maintenance procedures. However, for many users, several obstacles remain to achieving this outcome:• Equipment assets are not suffi-

ciently instrumented• Field instruments are not con-

nected to automation networks• Automation networks are

divided into data silos• Automation networks and busi-

ness 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 mon-itoring 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 tech-nology (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 con-trol and data acquisition (SCADA) system that also scans the control-ler, 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,

Figure 1. The complexity of fully integrating even a single I/O signal inhibits cohesive, cost-effective remote monitoring, resulting in fractured data and prolonged project timelines. (Source: Opto 22)



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 dispa-rate networks unless bridged by middleware. The lack of security further adds to the complexity, because information technology (IT) personnel need to take mea-sures to secure data after the fact with a plethora of firewall rules or network segmentations that com-plicate 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 net-works, 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 encryp-tion can reinforce existing network protections or be used to further consolidate the technol-ogy hierarchy.

Many edge computing offerings are appearing in the industrial automation space, most com-monly 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

Figure 2. Industrial edge computing devices like edge programmable industrial controllers (EPICs) simplify connectivity so that plant floor data can move seamlessly through the organization. (Source: Opto 22)



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 (Figure 3).

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 (applica-tion programming interfaces). It provides a large library of functions for building data flows that work intuitively with different connected devices, a wide variety of data-bases, and cloud IoT platforms. Node-RED simplifies the process of taking data from heterogeneous

sources, formatting it, combining it, and moving it into business sys-tems or down to the process.

OPC UA. Traditionally a com-ponent of automation middleware requiring a Microsoft Windows environment, the OPC standard has become available to edge devices via the platform-indepen-dent OPC unified architecture (UA) specification. OPC UA allows an industrial edge device to run an OPC server and commu-nicate with legacy PLCs directly using their native protocols.

This critical integration compo-nent makes it possible to deploy an edge communication network in parallel with existing automa-tion. Rather than requiring the usual rip-and-replace approach to modernizing infrastructure, legacy equipment data can be assimi-lated across disparate networks and data silos without interrupt-ing operations.

MQTT with Sparkplug B. Another open-source contribution from IBM, MQTT (formerly MQ Telemetry Transport) has become the most popular IoT-specific communications protocol for its flexibility and efficiency.

With it, IBM abandoned the traditional point-to-point, poll-re-sponse communication model in favor of a brokered publish-sub-scribe model. In this scheme, a

Figure 2. Industrial edge computing devices like edge programmable industrial controllers (EPICs) simplify connectivity so that plant floor data can move seamlessly through the organization. (Source: Opto 22)



central server (the broker) acts as a communication hub for the entire network. Rather than cyclically requesting data from field devices, devices publish data to the broker when they detect a change in a monitored value. The broker’s job is to send published field data to any registered subscribers, which can be software applications or other field devices. Think: Twitter for machines.

By routing traffic through the broker, instead of creating individ-ual connections between devices and applications, MQTT makes it much easier to share plant data with many different consumers. In an MQTT network, edge devices need to connect to only one end-point: the broker. The broker can then share data to as many autho-rized systems as desired.

For example, equipment data can flow from the plant floor to a local maintenance database, to a super-visory control and data acquisition (SCADA) host, and to a cloud analytics system simply by point-ing each application to a shared MQTT broker. Edge devices that comply with the Sparkplug B companion specification guar-antee interoperability, low tag management, and mission-critical state awareness.

BRINGING IT ALL TOGETHER

The growth of e-commerce, the demands of Big Data, and the birth of the IoT have produced an array of complementary IT tech-nologies that are filling a critical

need for industrial automation as well. As the industry moves toward digital transformation over the next decade and strug-gles to create its own industrial IoT, these technologies promise to resolve long-standing integra-tion problems left over from the previous generation of opera-tions technology.

The new approach to plant integration can be described in three steps. First, industrial edge computing devices form a secure, connected OT foundation, bypass-ing 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 auto-mation. 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 appli-cations 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.

Josh Eastburn is director

of technical marketing at

Opto 22 (www.opto22.

com). After 12 years as

an automation engineer working in the

semiconductor, petrochemical, food and

beverage, and life sciences industries,

Josh Eastburn works with the engineers

at Opto 22 to understand the needs of

tomorrow’s customers. He is a contribut-

ing writer at blog.opto22.com and can be

contacted at [email protected].

The approach that IT has developed for designing networks in response

to explosion in connected devices is called edge computing.



http://www.opto22.com

http://www.opto22.com

http://blog.opto22.com

mailto:[email protected]

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