With the right approach to IIoT technology, you can turn data into actionable information that helps ensure machines are self-diagnosing, provide transparency to end-users, and never fail.
Mark Densley, Director Business Development Factory Automation at Emerson
While the Industrial Internet of Things (IIoT) offers the promise of a revolutionary impact on manufacturing, implementation of the technology has presented real-world challenges that can slow adoption.
What holds us back from a broader embrace of Digital Transformation, especially in pneumatics? And, just as importantly, how do we move past that challenge to embrace the opportunity for real improvement in productivity and efficiency?
For end-users responsible for maintaining machines and keeping them running, the path is clear. An IIoT system provides data from the machine, ensuring its systems or components are working within their normal range. In that regard, the end-users are the ones pushing for IIoT implementation.
The disconnect is often with machine builders. They may have multiple customers pushing them for IIoT solutions, so OEMs are asking, “How do you implement that? Who has the product to do that? And what should we be monitoring?”
Pneumatics has taken advantage of diagnostic features for some time. For example, we can sense whether the power is too low or too high, or whether there is a short circuit in an I/O system that causes a failure.
Now, we move it a step further with sensors and the ability to use I/O systems to capture component performance data. The most common struggle causes people to ask questions such as, “I have all this data, but what does it mean? How do we turn that data into useful information?”
Understanding how this struggle creates challenges — and opportunities
The focus has shifted to how end-users convert that data into useful machine insights to react to, or even predict, failure. For example, programmable sensors can measure travel distance and velocity for pneumatic cylinders. They can sense the velocity of the piston in the cylinder, but can also use the sensor data to monitor the performance of cushions and shock absorbers used to dampen the load being moved by the cylinder. This ensures they perform within a certain specification window. If you interpolate that data, you can ensure the cylinder is performing as expected and quickly determine if it needs maintenance attention.
Such data can be seen in a PLC and, with an IIoT Edge Device, you can collate, analyze and aggregate the data to create actionable insights to provide continuous realtime monitoring of a machine.
Let’s look at energy consumption, for example. You can measure the system pressure and system airflow by using a technology like the AVENTICS AF2 Air Flow sensor, and correlate the result to an event and conclude that when you turn on a given valve, you get a certain flow. If the flow deviates from a derived nominal when the valve is on next time, this indicates something leaking within this circuit or actuator. For example, the seals may be wearing on the cylinder. You may know something is off, but what you do with that information and how you use it is the differentiator. As you know, machine builders seek to implement ways to leverage IoT capabilities.
The responsibility to address problems uncovered by the data falls on both the OEM and the component manufacturer. Success requires collaboration and support as well. OEMs and end-users would typically like to have some additional monitoring to make sure that their machine processes are being controlled correctly. Collaborating with the OEM to create an open IIoT architecture ensures appropriate sensors are in place and are sensing correctly to help keep that machine running full-time. This maximizes the end-user’s return on investment by lowering the total cost of ownership of the machine.
Involve IT experts early
Customers are increasingly familiar with the concept of a higher level of analysis and data sharing. More importantly, it depends on who you are speaking to within the machine design process. In the past, when we worked with machine designers, we discussed things like power and the air connection. Now, the discussion needs to involve factors such as network connections, security and VPN connections.
It is more important than ever to include the IT department in the design architecture and discuss what those connections look like and how the data are moving. Security issues are also important, especially to end-users, so involve IT experts early in the design to understand factors like how much data will be processed, where it is going and how it is going to connect.
Leverage partnerships for IIoT success
Component manufacturers outfitting the machine need to work together to understand how pneumatics come into play, creating a true partnership that develops the best IIoT solutions for customers. At the field level, pneumatics, drives, controllers and I/O systems must all work together. The key is knowing how the data are passing between those items and making sure that the data go to wherever they need to go, whether that is locally on a web server, or out to a cloud system.
Consider the energy consumption example again: The best way to realize energy savings is to use the correct compressor dependent on the required load. Although we may not produce compressors or control systems for compressors, we can tell the compressor control system what the demand for air is over the next 12 hours with smart pneumatics. And from that, the controls can manage the energy involved in producing compressed air accordingly, because it may not need all the air being produced. This is just one example of components and systems “talking” to each other to affect energy savings.
Put the pieces together for better pneumatics performance
How do these pieces come together to improve the situation for a manufacturer? One vision is to enable a highly autonomous maintenance process. That means the pneumatic circuit, or ultimately the machine, has 100% uptime and never fails. Obviously, components wear out; valves and cylinders go through their specified life cycles and need to be replaced. But the data are there to predict failure before it happens and prevent costly catastrophic failure.
For example, one application predicts the performance degradation of things like pneumatic valves. Based on a life-cycle benchmark and B10 life-cycle credits, we can use the data to track when a given valve will reach, for example 75 million cycles, and needs to be replaced. The operations team can receive a message about predictive failure and schedule when to replace the valve before production is disrupted.
Data from sensors also allows us to predict when a shock absorber on the end of the actuator is deteriorating by sensing an increase in the cycle speed, even by a few milliseconds. This would trigger an alarm or even send an automatic email to the component supplier that maintenance needs to be completed or that the component is ready to be replaced. The system could even generate an order and a new component would be automatically shipped to the customer, so the installation could be scheduled to minimize any production downtime.
Thus, with the right approach to IIoT technology, data can be actionable information that helps ensure machines are self-diagnosing, provide transparency to end-users, and never fail.