Predictive maintenance is, quite simply, the ability to fix what isn’t broken…yet. Through numerous advancements in data gathering and compiling, it is now possible to accurately predict when and how certain hardware components will break down. The key is, as with most everything in digital transformation (DX), data. Information is essential in the 21st century, and it’s more than just having a bunch of data points and sensor readouts – it’s knowing how to process it quickly and efficiently. This is at the crux of predictive maintenance, as well as digital twin.
Digital twins are virtual (or digital) representations of products, people, processes and even spatial environments. Once properly enabled, a digital twin can mirror and measure its physical counterpart. This means that, despite the perhaps static nature of their name, digital twins are dynamic - in constant communication and data exchange. Information flows from the physical to the digital twin, informing and updating it with real-time data, including any and all changes.
While not exactly new, digital twin applications are still evolving. More industries are discovering their potential and their practicality, all while understanding that this technology is far from mature. The advantages of digital twins are numerous and immediate, but none are so frequently front-and-center as the power of predictive maintenance.
Digital twins are not simple constructs. You can’t just install one on your browser and call it a day. Digital twins are made up of numerous different technologies – from IoT sensors to 3D CAD files to potentially augmented reality (AR) visualization, it’s really the product of an ecosystem of data communication. With all this constant visibility and measuring, a concept like predictive maintenance becomes not only possible but practical.
Sensors constantly monitor equipment at the component level, identifying and evaluating each aspect of operation. Should a component break down, this incident is recorded. This recording is logged in a complete and comprehensive history of the product in question. As this happens over and over again, across one or more devices, patterns start to emerge. This allows the digital twin to predict – with incredible accuracy – when and where the next breakdown will occur. The more digital twins there are, the more complete the picture.
What is happening now literally was not possible before, not even with an expert technician assigned to monitor the equipment as a full-time job. There is no downtime and no gaps in the data stream. By having the most complete picture, digital twins are providing complete transparency into the product life stream. Predictive maintenance is only possible with this level of visibility.
That said, digital twin benefits, amazing as they are in the predictive arena, are not confined to it. Remember, this is essentially a complete and total digitized readout of every important component in the physical, real-world instance. Even if an organization chooses not to pursue predictive maintenance capabilities, this readout will still be very useful for corrective maintenance operations.
For those unfamiliar with the term, corrective maintenance describes the traditional process of cause and effect. A machine breaks and then it is fixed. The reality is this can be a very economically and culturally draining situation, as breakdowns in products and processes often lead to serious consequences. As such, it’s important to get the repair right as quickly as possible.
The first-time fix rate (FTFR) is an enormously important stat for many executives and decision makers. It informs them of just how often the technician was able to solve the problem on the first try. Any downtime is costly, but downtime prolonged by second, third, and fourth attempts is far more expensive.
As such, anything that can improve diagnosis accuracy is appreciated. Since digital twins provide the most complete picture to date of the physical realities they reflect, this makes them valuable tools in the overall maintenance process, corrective or predictive.
The development and increasing deployment of digital twins will not just lead to more cases of predictive maintenance, which by itself is already a positive development, but for organizations fully embracing these practices – this shift will empower a decentralization of maintenance efforts overall.
This means more effectively hiring, maintaining, and deploying a technician force based on the actual reality of products and processes, rather than estimating what is needed and where the staff should be stationed. One technician with perfect information and complete visibility could, in theory, better service three manufacturing centers than three technicians in one plant operating on incomplete and frequently outdated data. This is not to say that fewer technicians will be needed – just to say that these skilled workers can now be better utilized for maximum efficiency and effectiveness.
Predictive maintenance as a concept hinges on a consistent stream of accurate information, and digital twin provides this foundation. It is possible to have digital twins without deploying predictive maintenance procedures. It is very difficult to effectively enforce predictive maintenance without the data that digital twins provide.
When it all works, it’s seamless. Just look at this video from client Harpak-Ulma to see the ease and efficiency of having data at your fingertips:
Learn how industrial companies are leveraging digital twin across engineering, manufacturing, and service.