Over the last several years, the mantra of “digitization is coming” has rung like a steady bell in the ears of both industrial manufacturers and the OEMs who support them. Although the theory surrounding how digitization can help OEMs to accelerate time-to-market, to optimize workforce knowledge, to increase productivity, and to lower downtime, has been a consistent topic of discussion, it has taken some time for practical solutions to appear that make a difference in how machine OEMs run their businesses.
The market has finally matured enough that disparate technologies can now come together, and the core elements of truly IIoT-ready devices, edge control, and software and analytics can now talk to each other, enabling OEMs to begin offering a new generation of digital services to their manufacturing customers.
Three areas where digitization is generating benefits for OEMs
Most OEMs find themselves in an environment where processes, and the tools that support those processes are not sufficiently streamlined to address time-to-market challenges. New digitization tools are now beginning to appear in the marketplace that simplify core processes such as engineering (configuration instead of programming), IIoT ready machine control and data analytics.
Regarding configuration of motor starters, for example, the way motor load management is applied to machine building has been redefined through the use of avatars (digital objects with integrated pre-programmed functions). For OEMs, this means a new way of engineering. It’s an innovative way of not having to program devices but, instead, focuses on simple configuration by functionality, which is far less time consuming and easier to maintain than custom configuration. The avatar approach eliminates having to look-up compatible components in catalogs and then configuring individual contactors. The simple action of dragging and dropping a function avatar (for example the avatar for the control of a reversing motor function) generates automatic access to libraries and pulls the required devices that enable the function to work. New configuration tools also automatically produce a bill of materials based on the avatar selections.
In the domain of machine control, new IIoT-ready PLCs now can function to support both logic and motion applications. Supporting both sets of applications with one PLC enhances flexibility while driving down cost. OEMs can now address a wide range of motion and logic-centric applications including packaging, electronics, material handling, hoisting and pumping. The new generation PLCs also set a new standard in digitization scalability, as the power of the CPU can be easily increased to extend the number of access points that the PLC can support. These new generation dual purpose PLCs are embedded with direct connectivity to the cloud. No translator or gateway in between is required. In addition, these new PLCs support open standards in order to greatly simplify machine to device, machine to machine, and machine to plant communication. Such unprecedented flexibility reduces the OEM integration work required when implementing new machines.
Analytics software represents a third area where OEMs can benefit from digitization. As machine performance data is gathered with the simple click of a mouse, that data can now be analyzed to identify behavioral anomalies. Such abilities open the door to new, lucrative monitoring services that OEMs can now offer to end users.
In this arena, two main categories emerged that are enabling unprecedented productivity:
- Machine diagnostic monitoring – Powerful algorithms automatically identify asset issues of the monitored machines. The algorithms are teached in the beginning with data of a reference machine behaviour which is considered as optimal. When the data start to deviate from “optimal”, this can be an early indication of failure. Results are then displayed in the form of case notifications for review and actions. A very important use case for machine diagnostic monitoring is to monitor relevant parameters of pumps, compressors or chillers like pressure, vibration and current data. With doing that, the analytics software is able to identify g. leakages in a cooling or a pumping system.
- Machine prognostic monitoring – Artificial intelligence algorithms teach the monitoring software, over time, what is normal and abnormal machine behavior. Measured data can be then automatically assessed to predict the future behavior of the machine. The more machine data gathered, the more reliable, precise, and powerful the prediction. For OEMs this could mean less time invested in building physical models to perform simulations. Through analytics software, performance issues can be addressed before they result in unanticipated downtime. A very useful use case for machine prognostic monitoring is to identify and measure data generated from servo drives and motors (vibration, current, torque) to predict the health of the corresponding mechatronic asset (servo axis, gearbox, robot arm, etc …). As abnormal patterns begin to be observed, like more friction and a higher than normal thermal load, support personnel is notified and the plant floor has much more time to react.
All of these new technologies can be linked via an open three-layer architecture. At Schneider Electric, we call this EcoStruxure Machine, an architecture specially designed to support practical implementations of IIoT-ready smart machines. To learn more about how OEMs and industrial manufacturing end users can benefit from available digitization solutions and related services, click here.