1. Data acquisition

Data acquisition for an IIoT platform is usually done through sensors/instrumentation and integration with existing data acquisition systems such as DCS/SCADA/PLC systems.

– What data should be monitored?

When determining what data points to monitor, first consider the business need or purpose. For example, is the intention to increase the throughput, avoid unplanned outage, or perhaps manage product quality? The first thing is to identify what data is important to be brought into the platform and is necessary for further analysis and reporting. Subject Matter Expertise (SME) knowledge should be leveraged in this stage to make sure the proper sensors are selected to provide meaningful data. For example, if you are looking to monitor health and efficiency of rotating equipment a SME might order a set of pressure sensors, accelerometers, and temperature sensors to perform appropriate calculations like determining head and monitoring the overall status of the asset.

– Does the sensor meet your requirements?

Make sure the sensor’s signal type, precision level, signal range and other specifications meet your instrumentation requirements.  If your Subject Matter Expert has defined which type of sensors are needed, work with them to make sure they have specified the precision and range. For example, monitoring pressure of one set of assets might require a 0-1000 PSI sensor, whereas another set of assets might require 0-10,000 PSI sensors – this could be the different between collecting meaningful data and having a broken sensor! Also consider the interface requirements for the sensor – different instrumentation, communication devices, and I/O boards may require different types of interfaces.

2. Communication technologies

There are many wired and wireless communication technologies available in Industrial IoT space with different characteristics with respect to bandwidth, transmission rate, and coverage.

– Wired, wireless, or hybrid?

Wireless is cost effective, but it is not necessary to assume it is the only approach for your IIoT deployment. Wired connections may be required for bandwidth, stability, or security reasons. In fact, maintaining a mixed wireless and wired (hybrid) implementation in IIoT architecture is quite common. Wired sensors often provide a much higher data acquisition and transmission rate which might be necessary for things like monitoring high frequency vibration data. On the other hand, pulling wires can be expensive and wireless can be a cost-effective alternative. In the example of vibration monitoring, a wireless sensor might contain an embedded processor capable of locally analyzing the high frequency vibration data — sending over consistent snapshots of data on a normal timed basis and sending data more frequently when a threshold is reached.

– How frequent and how much bandwidth is required?

It is important to understand the characteristics of the data being monitored from your assets, and determine what communication requirements are, such as bandwidth and cycle. Some data, such as temperature, may not change frequently, and therefore can be transmitted every 15 minutes, instead of 5 seconds, to reduce cycle and gain better battery life.

– How many assets and how big the facility is?

Low-Power Wide-Area Networks (LPWAN), such as LoRA, Sigfox and NB-IoT(Narrow-Band IoT), provide wider network coverage at with relatively limited bandwidth. For example, LoRAWAN supports up to 0.9 Kbps and can transmit data over several miles, which can be a good option for asset monitoring in large outdoor production facility. On the other hand, technologies like WiFi or Bluetooth can deliver high bandwidth at a shorter range. For example, Bluetooth supports up to 25 Mbps and 100 meter range, and is a great option for interacting with customers and data communication in a smaller setting like a retail store.

– Are there existing wireless technologies in the facility?

Although the integration can be implemented in different layers in the IoT platform, It can significantly reduce cost and allow tighter integration by leveraging existing infrastructure. Maintaining the same wireless technology in field can introduce synergies with effort in managing network security. Industrial protocols like WirelessHART and ISA100 are being implemented more broadly by many organizations and there may already be an accepted standard or even an implementation within your organization.

3. Integration with existing data acquisition system

Most industrial facilities will have some form of Distributed Control System (DCS) and SCADA in the facility which can be leveraged as a data source or data destination with your IoT implementation. This integration can impact the technical design as well as physical location of the IoT node/gateway. For example, it may be easier to install IoT gateway near the onsite DCS system due to distance limitations of cabling and hazardous area requirements.

– What protocol does the existing system support?

Legacy systems often communicate via common industrial protocols such as Modbus, OPC, or proprietary protocols whereas moderns systems might support REST, SOAP, and Websockets. As such, a custom protocol conversion interface may be needed to bridge the old and new. For example, a custom Proprietary-to-MQTT interface could be needed to communicate between an PLC and the IoT platform. Some IoT platform support a wide variety of protocols, so consider what protocols you will need to communicate with when selecting your IoT platform.

– Does the legacy system provide the desired level of data resolution and granularity?

Even though your IoT gateway can talk to the legacy system, don’t overlook if the level of resolution and granularity provided by the legacy system can meet the data analysis requirements. For example, vibration analysis may need greater resolution than the legacy system or chosen protocol can support.

As described in 3 Critical Steps for Industrial IoT Initiatives post, these systems are capital intensive investments, and can be costly to be replaced, so it is crucial to leverage them if applicable.

4. Edge, Fog or Cloud computing

Analytics, such as threshold analysis, custom calculations, or even machine learning can be centralized in the IoT platform, deployed in the plant, or deployed with close proximity to the asset. Deployments to an IoT platform typically provide improved scalability and reliability whereas deployments at the edge or on-device can provide improved latency and remove the dependency network access.

– How to optimize network usage?

Does the gateway or each individual node have enough bandwidth to transmit both raw and calculated data? Does it make more sense to process the raw data at near the asset and only send analysis results to reduce network usage and battery consumption? For example, instead of sending high resolution vibration data over to cloud constantly, it may be a good idea to only send average vibration reading to cloud when the overall vibration level is low, but send spectral data when the overall reading reaches a threshold to enable more detailed analysis of issues.

– How intelligent is the node and how fast are actions expected to be taken?

Some edge devices may be powerful enough to run basic logic control and even complex algorithms for data analytics, but analytics can also be shifted to another more powerful server with enhanced computing power and ample storage space. In many cases, people choose cloud computing for its scalability, simple management, and operational expense benefit over capital expenses. However, don’t forget to consider if it is required to run analytics in the event of losing connectivity. For example, an edge or fog computing may be required to predict unplanned pump failure in an expensive refinery process with or without access to network connectivity. And analytics results may need to be fed into the legacy control system and existing engineering processes to react to events in near real-time in which case an edge or fog computing solution may be a better fit.

– How much storage is required at the edge or gateway?

Storage can be important for data buffering during network outages or data processing that requires significant history, such as data modeling in machine learning. While network outages are not desired, they are common and it’s important to ensure the gateway or edge device has sufficient storage space to buffer logged data and sync without any data loss when connectivity is restored. Depending on the buffering requirement, it may make sense to move the buffering capability to the gateway or local server with much more storage space to buffer the whole facility’s data rather that buffering on devices closer to the asset.

5. Security, security, security

Never underestimate the damage poor security design can cause.

– How sensitive is the data?

What damage can it cause if the data is leaked or manipulated and how can the data impact business processes? For example, the leak of predictive maintenance data is bad, but may not cause immediate damage to the asset or business processes. On the other hand, if the analyzed data is fed into DCS, it could be used to shut down the whole process or cause damage – in this case, one-way, read-only communication may be necessary between the IoT gateway and DCS. Be careful not to underestimate the impact of the asset. For example, a cooling pump may not be an expensive asset, but can cause costly/dangerous damage to other assets in the process if it malfunctions or is maliciously controlled.

– How are security policies and controls defined and executed?

Things like system patching and password change policies are important, and need to be executed regularly to ensure the platform is up to date and secured. It is important to ensure system log reviews, access authorization updates, and system vulnerability scans are conducted regularly to identify potential issues.

Security needs to be thoroughly considered and implemented in every step and every component of an IoT platform. Since it may not be practical to develop proprietary security technology, which is often less secure, it is important to ensure industry standard security technologies and best practices are utilized wherever applicable.

For more technology examples used in each IoT component, check out our service page. What special considerations do you make for Connectivity in your IIoT implementations? Sound off in the comments below.

1. Document Requirements and Regulations

A successful initiative starts with strong documentation and understanding of requirements and limitations. Some of the best projects start from a simple idea, but it’s important to write down the requirements as it will help flesh out details and get everyone on the same page. Take time to understand what the needs are – they might stem from a set of business needs, technical needs, or even personal needs.

Understanding the goals of the leadership team goes a long way and pairing those expectations with feedback from the field will further contribute to success. Analyze how maintenance and operations teams are doing their jobs today – if possible, get out in the field and watch how the team does things. Talking with the people in the field goes a long way to understanding where pain points are and what kind of limitations or operational parameters might be in place.

Subject matter experts (SME) can also help identify regulations or requirements that are critical to a project’s success. Engage SMEs early on, document requirements, and validate that the final output of your project meets all regulations. Understanding these requirements will help you decide how to manage your project. For example, you may be able to proceed with a pilot in a general area classification before going through costly regulatory bodies for hazardous area or safety approvals. In other cases, you might find that the costs outweigh the benefits and the project can be adjusted or scrapped accordingly.

2. Strategically Consider the Business Needs

When exploring which assets to monitor and which systems to integrate with, keep in mind your goals. If you are looking to reduce costs, pull the list of systems with the most maintenance events or highest maintenance costs over the past 18 months. If you are looking to add new value – analyze your production systems to see which lines could net higher yield or additional throughput. As you pull this data, study which monitoring systems already have data for these assets and which other enterprise systems are important to understanding the lifecycle of the asset – this might include maintenance systems, ERPs, and line of business systems.

As you review these business drivers, you’ll often find answers to questions you hadn’t even previously considered!

The vast majority of the leadership teams we talk to highlight their investment in people as one of their company’s greatest assets. As you build out the roadmap for your IoT investment, consider the needs of your people – what systems do they use most today, what processes do they follow, what pain-points do they have today. The same concepts which apply to gathering feedback from your customers (Voice of Customer) can be applied internally to gather deep insights on where improvements can be made to significantly reduce costs and drive value.

And if we’re talking about connecting sensors, assets, and monitoring systems we would be remiss if we didn’t talk about getting the Operational Technology teams and Information Technology Teams on the same page (OT and IT). Each team often has a different set of drivers and often times these groups feel more like competing sports teams than two groups working for the same organization. The good news is that these teams are employed by the same company and ultimately have the same shared interest of making the company successful – whether that be maintaining servers or maintaining industrial assets, the shared benefit is for the organization. These are often the same teams who will provide guidance and support with security and getting the right people on board will help ensure your implementation is safe and secure!

3. Connect the Old and the New

World-class organizations take careful consideration in managing their investments in equipment and systems. These are often capital intensive investments and ripping out all the existing investments to replace with shiny new systems can significantly increase cost and complexity. That being said, when there is a new system or component which adds significant value to the value-chain, organizations should evaluate which legacy components have exhausted their useful life or are close to doing so as a cost-benefit analysis might suggest replacing the legacy system.

Consider your legacy equipment – what physical or digital interfaces do these systems have? Does your equipment already have embedded sensors that you can tap into through industrial protocols like Modbus, Fieldbus, OPC or even 4-20 mA signals? If so, there are plenty of platforms that support integration with common protocols out of the box or you can ask a System Integrator (like us!) for help. And if you need to bring your own sensors, consider wired and wireless options – there are many products which support industry standard wireless protocols like WirelessHART or ISA 100.

Analyze which monitoring systems you’ll need to integrate with. Depending on your needs, the data might need to go to an existing DCS/SCADA system, historian, production system, or other monitoring system. Do you need to integrate data from new sensors and data acquisition systems to your existing systems? If so, what protocols does the system support and what level of resolution and granularity is required? As you start with a pilot, determine if integrating with the legacy systems is a critical path enabler or if simplifying scope and focusing on the IoT platform better fits with your business goals.

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Whether you are already deep into your IoT implementation or just getting started, these three areas are key points of consideration for having a successful Industrial IoT implementation. What topics do you consider most important to an IoT implementation? Let us know in the comments below and check out our article on the Essential Eight Tech Megatrends.

1. Artificial Intelligence

Using computer systems and technology to perform activities that would otherwise require human intelligence. While many people immediately think of futuristic movies with lifelike robots that can interact on a human level, this technology is available today and being used in ways that allow humans to focus on more challenging problems. Forms of artificial intelligence have been around since the 1950’s and became popular with large tech companies during the 90’s and 00’s. Companies like Google have leveraged the power of artificial intelligence in the form of machine learning to power smart search engines, powerful mail filters, and even automated assistants.

Use Cases:

• Predictive Maintenance
• Natural Language Processing
• Security
• Recommendations
• Anomaly Detection

2. Augmented Reality (AR)

The combination digital information with reality – typically by layering data, imagery, audio, or haptic feedback onto the real world via a computer device. If you’ve ever watched Monday Night Football, you’ve likely seen the yellow first down line digitally overlaid onto the field – this is a form of augmented reality. One of the more common use cases of Augmented Reality is to combine digital information or imagery overlaid on top of the real world – either through glasses or a device with a camera. Common examples of AR include Microsoft Hololens, Google Glass.

Use Cases:

• Technical Analysis
• Maintenance
• Training
• Marketing
• Product Design

3. Blockchain

A distributed, immutable digital ledger of data records. While blockhain technology has historically most often been associated with Bitcoin – a digital currency – it’s now being used on many other areas including IoT, healthcare, and big data. And if a digital ledger sounds a lot like a database, that’s because blockchain is a lot like database. Some of the key features of the blockchain are it’s distributed nature and immutability. A blockchain is run by many different servers instead of a centralized set of servers and once data is entered into the system the data can’t be directly overwritten. In order to change data, an additional entry has to be made, much like an accounting ledger, and all of the history remains providing traceability.

Use Cases:

• Internet of Things
• Big Data
• Distributed Databases
• Financial Sector
• Healthcare Sector

4. Drones

Formally known as Unmanned Aerial Vehicles (UAVs), drones are aircraft which are piloted remotely or by onboard computers. Drones are beginning to enter mainstream as both recreational items for consumers as well as powerful tools for businesses. Companies like UPS and Amazon are working to figure out how drones can be used for fast and cost-efficient package delivery while farmers and industrial companies are leveraging drones to remote inspect and surveil areas that could previously only be reached using high cost and high risk methods.

Use Cases:

• Package Delivery
• Facilities Inspection
• Wind Turbine Inspection
• Surveillance

5. Internet of Things (IoT)

The network of interconnected physical devices embedded with sensors and technology enabling the sending and receiving of data. And while the topic has the word Internet in the title, the key focus is on connecting physical devices to a network to be able to send and receive data. This network can be a private network within an Enterprise or connectivity over the public Internet – typically via secure tunnels or encrypted channels. Many organizations are changing their service organizations over to Connected Services – wherein they are leveraging the power of IoT to enable new service models and new methods of delivering value to their customers and shareholders. Some products have sensors that are lying dormant or only used intrinsically within the product and these sensors can be connected to the network unlocking new insights and business models. In many cases, products are being Smart Enabled either from the factory or by using aftermarket wired and wireless sensor technologies.

Use Cases:

• Remote Monitoring
• Predictive Maintenance
• New Business Models
• Warranty Mitigation
• Customer Intimacy

6. Robots

Mechanized machines capable of carrying out procedures automatically or via programming. While the manufacturing space has been leveraging robots for decades, other industries are beginning to realize how robots can be used to automate redundant simple tasks as well as highly complex tasks that require a high degree of precision. Amazon purchased robotics company Kiva Systems in 2012 and doubled the amount of robots being used in their warehouses between 2014 and 2015. Worldwide, the growth of robotics in the industrial space has continued to increase at a rapid pace growing from 60k industrial robots shipped in 2009 to 239k shipped in 2015.

Use Cases:

• Manufacturing
• Agriculture
• Safety / Military
• Hazardous Area Inspection

7. Virtual Reality (VR)

Fully immersive digital environment generated using computer graphics and audio. Like Augmented Reality mentioned above, Virtual Reality uses computer graphics and digital capabilities – the difference is that with VR, the user is fully enveloped in the virtual world rather than having digital imagery overlaid on top of the physical world. Common examples of Virtual Reality include Google Cardboard, Samsung Gear VR, Oculus Rift, and HTC Vive – these headsets allow the wearer to be immersed in the digital experience and as they move their head to look around they are able to look around and interact with the virtual world. Organizations like Flowserve, a large industrial manufacturer, are leveraging virtual reality to create game like applications to train employees and customers using the real controls they would use on expensive industrial equipment. This allows them to simulate costly failures and upset conditions without experiencing the real cost of the failure.

Use Cases:

• Training
• Simulations
• Product Design
• Virtual Tours

8. 3D Printing

Creating a physical object from a digital model – typically by laying down multiple layers of a material in succession. Also known as additive manufacturing in the industrial space, 3D printing creates physical objects by adding material (often starting from nothing) rather than starting with a block of material and removing material to create the end product. The technology has evolved from printing small plastic objects to being able to print materials of all types including metal and high strength / high temperature materials that can be used in tough industrial environments. Companies like Nike are using 3D Printing to quickly move from idea, to prototype, to working product – they can print a 3D mockup of a design in a few hours which allows them to get hands on with a product to understand how it feels in the real world and what changes might need to be made.

Use Cases:

• Product Design
• Prototyping
• Parts Manufacturing
• Rapid Manufacturing
• Reverse Engineering