Powered by smart machines, the new industrial revolution is changing how machine builders design, and how manufacturers operate today and in the future. To remain competitive and profitable, plants and machines will have to be smarter: better connected, more efficient, more flexible, and safe.
Two trends creating a lot of buzz today are about to unleash a profound change throughout global industry: Industry 4.0, the German-based approach for smart manufacturing, and the Industrial Internet of Things (IIoT), with a focus on connected devices and analytics. Our focus here is on how industrial automation and machinery will be impacted.
There are some obvious similarities and differences between IIoT and smart manufacturing (Industry 4.0), as well as areas of convergence. Smart manufacturing initiatives are focused on manufacturing flexibility, increasing automation levels, and digitization. In the long run, this will reshape complete factories and the way they operate. Such evolution requires embracing a multitude of technologies and ideas that will have a massive impact on end users and OEMs. This will take some time and IIoT, with all its connected devices, will act as a key enabler.
The IIoT vision of the world is one where smart connected assets (the things) with varying levels of intelligent functionality — ranging from simple sensing and actuating, to control, optimization, and full autonomous operation — operate as part of a larger system. These systems are based on open and standard Internet and cloud technologies that enable secure access to devices and information in order to leverage big data and analytics, and mobility technologies to drive greater business value.
OEMs and end users can leverage IIoT to better monitor and control machinery. Within industrial environments, some devices today are connected, but many are not. IIoT applications will include not only machine-to-machine (M2M) communication, but also machine-to-people, people-to-machine, machine-to-objects, and people-to-objects communication. These connections enable the ability to collect data from a broad range of devices and applications. This “big data” can then be accessed via the cloud and analyzed using sophisticated analytics tools.
Some of the elements that encompass IIoT have actually been around for quite some time; for example, communications. This is why IIoT should be viewed as more of an evolution rather than a revolution. New elements like the cloud, cybersecurity, big data, and pervasive sensing are only just now reaching levels of maturity that enable widespread adoption. The challenge is how to implement all of these disparate yet connected elements into an industrial environment.
IIoT initiatives are also converging with some of the new smart manufacturing momentum. The basis for both of these market trends is to enhance networked resources so that distributed intelligence can lead to improved visibility and management of production. In order to benefit from the potential that now exists for the development of new levels of operational intelligence, industries will need to migrate over time to a plant infrastructure that enables the exploitation of these new capabilities. This is where the next generation of machines — the “smart machines” — enters into the picture.
Manufacturing floor machines will evolve their level of intelligence in order to accommodate more predictive planning and more flexible business needs. The term “smart machine” implies a machine that is better connected, more flexible, more efficient, and safe. Based upon a collection of smart, connected products, it maximizes efficiency via intuitive collaboration with its users. A smart machine is also capable of participating in predictive maintenance practices while minimizing its own environmental footprint and total cost of ownership. Smart machine development is influenced by three principal drivers: technology, consumer market trends, and end-user demands.
On the technology side, both innovation and lower costs are making new generations of equipment accessible to industrial sites in need of migration. Some highlights are Ethernet connectivity that enables integration of networks and improved data access; wireless (e.g., RFID) for rapid, automatic data entry; mobile technologies that allow for safer, more remote operation of equipment; increasing CPU power so that more throughput is enabled at lower cost; multiple Ethernet ports on automation devices; memory cost decreases for advanced data management and better decision support; digitization for low-cost development of machine automation simulation programs; reduction of component footprint and heat dissipation; ability to connect a wider range of actuators and smart sensors for gathering more accurate data; and augmented reality and biometric recognition that improve machine-operator interaction and security.
The release of new technology increases the expectation of machine operators and system users. More mobility is increasing the use of mobile and wearable devices to gain access to information at any location. In addition, machine operators expect devices (and machinery) to be plug-and-play (e.g. as easy to use as an iPhone, USB stick, or Bluetooth device). End user demands for ultimate flexibility will drive the manner in which IIoT applications are designed.
Immediate accessibility to information (50% of maintenance spend today is focused on searching for information), and assurance of security and safety in order to protect user and machine will be required. Tight control of production costs and the improvement of overall production line performance will also require machinery that is more functional, flexible, connected, and efficient.
Ethernet-based networking of components and resources is a key element of smart machines. Open standards will be a key enabler of adoption, allowing integration of systems, better visibility, and an overall improved level of business control.
Characteristics of Smart Machines
The four key characteristics of smart machines are efficiency, safety and security, flexibility, and connectivity.
Efficiency — With the use of sensors and the intrinsic knowledge regarding its own capabilities and features, a smart machine will be able to monitor its own key components as well as environmental conditions. Embedded intelligence will correlate upstream and downstream behavior and adapt its own parameters within given business rules. By providing relevant information to operators, connected data consumers at the OEM, and the end user, the smart machine enables manufacturing lines to produce in a more reliable, flexible, and efficient manner. Machines at the forefront of development will increasingly use sensors, both wired and wireless, with embedded intelligence helping to distribute and automate decision-making on the factory floor.
This level of machine monitoring also enables preventative maintenance supported by the OEM, helping to avoid component failure and associated downtime, or damage to the machine or components. It also allows for maintenance to be scheduled to minimize the impact on production while increasing business opportunities for value-added services.
Smart machines must have the appropriate level of intelligence to assess data quickly and in a decentralized fashion. Routing all data to a central control for analysis will quickly lead to delays as it is a non-scalable structure. Sensors, components, and machinery with the intelligence to only share data that falls outside of set parameters will lead to better overall data management. Improving the level of data shared with the broader network/community will accelerate decision-making and reduce backlogs (where critical information could be delayed or missed altogether).
Storage of data is also an important consideration. To date, hardware has largely been used to store production data, but this method can be very time-consuming and expensive to manage. The cloud is increasingly becoming a viable option to help better manage data in a more cost-effective manner.
Safety and Security — With security built into their fundamental designs, smart machines will improve the safety of operators and minimize the security risk of increased networking. Improvements in machine performance and lifetime cost reductions cannot be offset by reducing the safety or security of the machine or production line. In terms of safety, machine builders need to offer a broad range of flexible options. This will include dedicated safety components, such as laser scanners and safety cameras, together with automation components with embedded safety, such as safety PLCs and safety drives. The ability to utilize a mix of safety components and controllers will allow machine builders to fit the solution to specific end user application requirements, helping to improve overall performance and productivity.
Today, data security is the leading inhibitor of end user adoption of new networking technologies and work processes. The perceived risk of networking components and machinery in order to achieve production benefits is high. Particularly with IIoT and increasing levels of connectivity, security needs to be considered at numerous levels. Security provision needs to be multilayer, incorporating hardware, software, and services. Machine builders (and automation component vendors) need to assure that end users are aware of security vulnerabilities, and can manage network infrastructure to minimize the risk of a breach.
Flexibility — Any new smart machines will need to be compatible with the existing installations or machinery from multiple OEMs; end users want devices that can be installed within a short timeframe. Integration into the rest of the system must be easy. The lifecycle of today’s machines does not allow monolithic or single-purpose design. The fast development driven by time-to-market constraints force OEMs to shift towards mechatronic design and modularity. Smart machines will benefit from templates of proven design from simple software functions up to fully functional modules describing mechanics, electrical, motion and interfaces, features, and behavior.
Modularity is one enabler where the paradigm to reuse software and hardware in a different context requires a new level of thinking. The concept of clear and strict interfaces with well-defined behavior that can be tested comes from the IT world, and finds its space in automation with some adaptation.
Connectivity — Smart machines will connect directly to the broader (Ethernet-based) network. This enables data sharing and production planning, which goes far beyond the capabilities of traditional standalone machinery and automation. Smart machines will bridge the information technology (IT) and operations technology (OT) gap, making available production data that can be used in numerous management settings. Machine operators and factory floor engineers are embracing in ever greater numbers the concept of using mobile devices at work. Personnel no longer need to be in close proximity to a machine in order to monitor or manage performance. Machine engineers can also diagnose problems and offer guidance remotely, which also speeds up implementation of a solution. This reduces downtime and losses from component failure.