Programmable Logic Drives Industrial IoT

The Internet of Things (IoT) is poised to completely transform the way business is done and companies compete. Perhaps the biggest impact of this revolution will be within the Industrial Internet of Things (IoT) where factories will evolve into smart factories,  leveraging intelligent distributed autonomous control systems powered with Edge Analytics. While today’s standard closed loop control systems are designed to  respond much quicker and with more precision than a human operator could, the new Edge-analytics and Fog-computing enabled control systems will dramatically improve production efficiency by using big data analytics, fueled by an increased number of sensors, to spot trends and to autonomously implement continuous corrective and optimizing changes in local and global plant-control strategies, in order to increase efficiencies, reduce operating costs, decrease downtime and improve asset utilization And this revolution is probably much further along than you realize.

A Quick Introduction to the Industrial IoT

The Industrial Internet of Things had its beginnings when the factory floor began installing connected electronic sensors and actuators to help manage a wide variety of industrial processes. Measuring temperature, pressure, transportation speeds, in real time, and providing this information to centralized process controllers provided dramatic improvements in operational efficiency, safety, and reliability. Connectivity was the primary element in these early Industrial IoT installations.

As the number of sensors and controllers grew, the amount of data grew as well. There were significant advantages in keeping this data around so that trends could be uncovered. These trends could be used by system managers to further ‘fine tune’ processes for improved efficiency and to spot aging and wear effects that might lead to unscheduled down time or even system failures. As the Cloud developed, it provided a useful mechanism for analyzing and storing this vast amount of data. Managers could use complex data analytics to determine trends and take action. Often these analytics, and the efficiency benefits they provided, became an increasingly important aspect of managing industrial process and were considered key Intellectual Property of the manufacturing company and could even provide a significant competitive advantage. This entire scenario was fueled by an over 2x reduction in the cost of sensors, and rapid reduction the cost of storage in recent years, and up to a 60x reduction in the cost of processing and up to a 40x reduction in the cost of bandwidth over the last decade.

The below figure illustrates how the connected factory floor can be integrated with the Cloud and the associated network infrastructure and protocols are overlaid on the familiar factory elements. The figure also shows how data flows from the sensors and sensor-fusion systems on the ‘edge’ of the system up through gateways, the enterprise and into cloud based storage and data center processing. Data flows in the other direction too, so that actuators can take appropriate action to modify process –control activities. (Notice that this structure is being used in a factory floor example, but a similar approach can be used for everything from smart buildings, the energy grid or even a modern hospital).

Figure 1: Illustration of an Example Industrial IoT Implementation for an Intelligent Factory Floor (Image courtesy of Niladri Roy, Altera) 

Autonomous Operation Improves Efficiency

The importance of smart data analytics, based on years, or, in some cases, even decades of experience to creating rule-based systems that can improve operational efficiencies can’t be overstated. This is where experience can really pay off. Unfortunately, when a human system manager is in the experience ‘control loop’ you sacrifice the ability to respond quickly to changes in sensor data. And the amount of data generated can be overwhelming to a human operator. What is needed is a form of autonomous control, working quicker and more precisely than a human system operator can. Perhaps the concept of autonomous operation and how it can dramatically improve IIoT based factory efficiency is best illustrated with an example. Asset monitoring is a familiar concept and good illustration of how autonomous operation can improve efficiency.

Asset Monitoring

Sensors can not only keep track of the various processes involved on the factory floor, but they can also monitor the equipment being used in the manufacturing process. Machinery wear can be an important part of asset monitoring and simple hours on-line or amount of material processed can provide a first degree of information.  More importantly however would be the collecting data on the quality of the output, and responsiveness to changes in production parameters. When motors or pumps are involved, measurements of heat, vibration, end even electromagnetic flux can provide useful data for spotting trends and predicting wear. When these types of analytics can be moved closer to the sensor, perhaps located within the motor or process controller, and data-acquisition combined using sensor fusion algorithms, autonomous control of the asset can be implemented. In this type of system, wear and fatigue information can be used to modify the control algorithm to improve operational efficiency without the need for ‘central control’ to manage these low-level decisions. As wear becomes more of a concern, perhaps when pre-defined ‘trip levels’ are the reached, the autonomous controller can report this to central control for preventative action.

Even autonomous controllers will supply important sensing data to the cloud,however, to be used in big data analytics. Measurements over an entire factory, or a number  of factories, and over a significant amount of time can provide very valuable insights into wear and its correlation to and prediction of possible failures. This information might be sufficiently valuable to encourage third parties to collect it, analyze it and then offer recommendations or predictions to the factory owner. When sensor data is combined over multiple factories and multiple types of operations big data analytics can sift through the wealth of data and create new models. These models might result in new trip points or new fusion algorithms programmed into autonomous controllers to further improve efficiency. Maybe these third parties would even collect a percentage of the savings that result from their algorithm improvements!

Increased Requirements for Computation and Connectivity

This somewhat simple example given above illustrates the potential for more intelligent Industrial IoT systems and the dramatic improvements in efficiency and profitability they can have on the smart factory. The growing number of sensors and actuators required to provide the ‘big data’ needed to effectively implement these types of systems will require a more robust computation and communications infrastructure. More sensors and actuators means more data flow and an increased need for high seed communications. This will also mean that additional data aggregation is needed to combine data traffic onto higher performance networks and requires more processing and storage. Secure communications for data traffic and to protect remote updates from hackers is also an increasing concern. Finally, the growth in the use of wireless sensors, repeaters and aggregation nodes is another reason for increased computation and communications capabilities. 

Altera Solutions

Industrial IoT implementations will require high computing power and significantly increased communications bandwidth to create the intelligent and autonomous communications systems needed to support the dramatic growth expected in sensor and actuator deployments.  These new capabilities will need to be delivered without impacting the limited board space and low power budgets required by the standard ‘footprints’ associated with smart factory installations. Altera solutions have significant advantages when increased computation power, and improved bandwidth are needed while keeping within tight budgets for both board space and power. 

The Altera Cyclone V SoC has the performance and high-speed connectivity to run the control (PLC), HMI, Gateway and secure cloud connectivity in a single device while using less than 3W for the entire PLC board. Because Altera SoC FPGAs can implement high-performance algorithms and HMI functions in hardware using parallel operations for much higher performance and at reduced power budgets when compared to conventional processors. Altera SoC FPGAs can also integrate multiple Industrial Ethernet protocols, and even high-performance interfaces for back-bone connectivity, using ready-made intellectual property (IP) cores and the on-chip Hard Processor System (HPS) to run standard protocols. Figure 2 below shows the details of a single chip PLC implemented on an Altera SoC.

 
Figure 2: Altera SoC Implementing a Single Chip LPC

The Altera MAX10 non-volatile FPGA does not require external configuration memory, minimizing board space to fit in small I/O module applications. Further board space reductions are possible when the on-chip user flash memory can be used for code or data logging storage and the on-chip analog to digital converter (ADC) can often eliminate the need for an external device used in common industrial analog interfaces.
Additional details on Altera solution advantages can be found in the references given at the end of this article.

Getting a Head Start on an Implementation

Arrow offers a wide selection of Altera development kits and evaluation platforms to get you started on your implementation. Visit the following Arrow web page to view Altera kits offered by Arrow. 
Conclusion

The continued build out of Industrial IoT smart factory installations will need intelligent and autonomous connectivity and control to keep up with the growing deployment of sensors and actuators. Altera solutions offer the computational performance, high-bandwidth, low power and small board space needed to efficiently implement this new class of not just smart, but intelligent factory systems. 

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