They say a picture is worth a thousand words, so here’s a hypothetical situation to paint the story ‘how real-time information and predictive analytics unlock value.’
To start, imagine a fully functioning assembly line with a robot, pneumatic system, a series of conveyors, and a vision system.
Lets pretend the supply station in the back is bringing in our raw materials. The robot is assembling those materials with precision. The resulting assemblies are than passed on to the quality station, and the vision system inspects each of those assemblies to insure proper alignment of the parts.
This is a pretty generic operation, but it can show how unified real-time information and predictive analytics unlock value.
Now imagine yourself as a maintenance engineer, who wants to check the status of your asset pool.
Using a software, such as ThingWorx Navigate by PTC for example, you launch a role-based maintenance application. All of a sudden you see a complete list of your assets with real-time performance stats and relevant alerts or notifications. You also have a complete list of all your outstanding maintenance work orders.
From here, you have the ability to drill into any of your assets, but you start with the quality station. You immediately see the key characteristics of the station. You see that speed vibration and temperature are all operating within their specified range. You could also see notifications of any warnings, malfunctions, or potential future problems.
Next, you use your device to take a look at the pneumatic system. The pneumatic system also looks fine. Both pressure and flow are operating within the specified range, and there are no outstanding maintenance tickets or work order notifications on your screen.
Now, let’s consider a situation where there was a leak in the pneumatic system. Let’s say a loose fitting was releasing pressure, a fairly common problem in pneumatic systems. Now, rather than looking fine, your device displays flow readings outside of the designated operating range. Furthermore, an alert has automatically been sent to notify you of a system has an error. The overall status indicator on your screen has now switched from green to orange – operational, but not optimal.
Your software solution’s machine learning is now predicting that this air leak, if not repaired, will result in a pneumatic gate failure in approximately 10 day’s time. The good news for you is the system has already issued you a maintenance work order address the problem before asset failure and unplanned downtime.
This scenario is made possible by a system equipped with primary and secondary sensors, and a complete Industrial Internet of Things (IIoT) solution that can turn raw machine data into valuable information.
For example, your pneumatic system has an air flow sensor, as well as a pressure sensor. The conveyor systems are equipped with motor temperature sensors and vibration sensors.
You have also used your software to integrate manufacturing floor systems with a real-time IT applications, asset maintenance tools, and ERP systems. This provides you with a real-time alignment of your IT and OT systems.
Now, all of your systems are throwing data out at a staggering 800 data points per second.
Your software’s machine learning then uses that real-time streaming data to establish a baseline of normal operating conditions. This way it can immediately connect and broadcast any anomalies that occur. It uses these anomalies, in conjunction with its prediction capabilities to notify you of future problems, just as in the case of the pneumatic failure.
Now that you have an understanding of what is happening under the hood, let’s take a look at how all this comes together to enable real-time operational intelligence.
Pretend you are a production manager. Using software like ThingWorx Navigate and Kepware you have complete visibility into all of your factory operations. You can see all of your work orders, lines, and all of their critical KPI’s.
On your device you notice an orange status indicator on line one (that was created from the air leak earlier). Once that air leak has been repaired, everything returns back to normal, just as you would expect.
Let’s explore one more hypothetical situation. Consider yourself to be an operator. In this case, you have just been assigned a new order for a thousand units that need to be delivered and expedited for an end of day delivery.
You’re notified of the order and in this smart connected scenario you, as an operator have a single portal from which you can see and execute all of your work. Through a single pane of glass you now have access to your business systems information and your operational data including the KPIs from your line.
On your device you also have up to the minute visibility of the OEE (Overall Equipment Effectiveness). You see real-time data measurements of your manufacturing operation’s availability, quality, and performance.
Let’s see how some of these metrics might change if we go ahead and speed up the line to accelerate the current order, in order to make room for that expedited order.
To do that you switch the line speed from level one to level two. What you see in seconds on your device is that line speed has increased, and your assemblies are still passing the quality check.
Within a couple minutes and a few additional cycles, on your device you see both your performance and OEE trending upwards.
As an operator you now are assured that you are going to meet your end of the day deadline.
Using these hypothetical situations, together we have painted a picture demonstrating how you can connect disparate assets from different vendors, to provide real-time information.
You’ve also seen how you can leverage role-based applications that combine business systems information and operational data to empower your workforce with real-time actionable intelligence.
By integrating machine-learning capabilities you brought a whole new level of predictive intelligence to your factory floor, identified problems, and resolved issues with minimal impact on operational performance.
This is exactly how real-time information and predictive analytics can unlock value for your organization.
I have a twin! Well, I have a digital twin. You probably do too. If you’re unfamiliar with the concept of a digital twin, don’t fret—you’re not alone. In fact, this technology is relatively new and still developing.
The idea of creating virtual models to simulate real-life situations isn’t new. NASA uses digital twins to run simulations and test flights on airplanes before they’re actually flown by pilots in person or sent into space with astronauts aboard them (pretty cool right?). However, until now there hasn’t been much focus on how we could apply these same concepts outside the aerospace industry — until now that is…
The idea of a digital twin is simple to understand. A digital twin is a virtual model of a process, product, or service that can be used to:
- Improve performance: Understand how a process works, and improve it.
- Explore new ideas: Imagine what could happen in the future, and create it now.
- Make better decisions: See what’s happening on the ground in real time, so you can make confident decisions for your business.
- Reduce risk: Identify potential problems before they occur and fix them before they cause issues for customers or colleagues.
- Improve efficiency: Maximize resources to get more out of them than would be possible otherwise – whether that’s staff time, materials or energy consumption – by turning data into insights for everyone involved in a system (including those who aren’t currently involved).
Digital twins are used to run simulations using predictive analytics and data from sensors that are attached to airplanes and engines. These “test flights” for engines and airplanes allow for safe experimentation and troubleshooting without risking human life or harming the equipment. More recently however, the potential use cases for digital twins have expanded beyond industry.
NASA’s journey with the digital twin
NASA’s Advanced Turbine Systems Project (ATSP) has created a digital twin of their Pratt & Whitney PW1000G geared turbofan engine used in aviation systems like Boeing’s 737 MAX series aircrafts. This makes it possible for engineers at NASA’s Glenn Research Center in Cleveland, Ohio to monitor real world conditions on an airplane remotely via computer software without having any physical connection between themselves and the airplane itself – all from their office desktops!
Digital twins aren’t limited just to planes though – they can be applied anywhere where there is an application that would benefit from being able to predict future outcomes based off current data gathered through sensors placed around said device/application/process etc…
Today, digital twins are being used in healthcare to help monitor a patient’s health in real time. Augmented Reality (AR), simulated environments, and virtual reality (VR) can all be used with the data provided by digital twins to improve patient outcomes. For instance, AR could be used by surgeons during an operation or VR can be used by physicians to practice risky procedures in a simulated environment before they operate on an actual patient.
The list of potential uses for a digital twin is seemingly endless, but one thing they all have in common is their ability to collect data. For example, an AR system could be used by surgeons to visualize a patient’s anatomy in real time and allow for better planning of surgical procedures.
Virtual reality (VR) can be used by physicians to practice risky procedures in a simulated environment before they operate on an actual patient. The benefits of this approach include the reduction or elimination of unnecessary risks during surgery as well as the reduction or elimination of costs associated with conducting unnecessary surgeries that did not need to take place because the physicians were not sufficiently trained prior to operating on real patients (which can lead to malpractice lawsuits).
The idea behind digital twins goes beyond the practical uses of this technology—it is rooted in the desire to create a more connected world where people’s decisions can be made with better information than what has been available in the past. When we’re able to see how our choices impact different systems—for example, seeing how changing one variable will affect overall energy consumption—we gain better insight into how we can create a more sustainable future.
As you may have heard, a digital twin is an avatar that represents your physical system. It’s kind of like an actor who plays the role of “you” in the virtual world and learns how to be more efficient, safer, and easier to use over time. This concept can be applied across systems ranging from trains to buildings to entire cities. Since all systems are made up of parts that must work together in order for a system as a whole to function properly (think about how many things need to go right just so you can take a shower), it makes sense that we’d want an accurate representation of those parts—and their interactions—in order for us humans running them not to make mistakes or waste energy unnecessarily.
As we’ve seen in this post, digital twins can be used for many different purposes. The technology has already been applied to industrial processes, healthcare, and the energy sector. In the future, we’ll likely see more uses for digital twins in retail and other industries as well. What will your digital twin look like?