Predictive maintenance has been changing the world of manufacturing. By creating a situation where manufacturers can avoid costly, untimely, and unexpected equipment breakdowns, predictive maintenance is increasing productivity and efficiency by reducing time spent offline and ensuring that machines work near, or at, full capacity at all times. Predictive maintenance seeks to establish when equipment failure might occur and allows for the prevention of that failure through scheduled maintenance. Using technologies like machine learning, smart sensors, and an interconnected network of machines, operation and maintenance costs can be reduced and the loss of productive hours will no longer be a problem.
Given that unexpected downtime costs the industrial manufacturers an estimated $50 billion a year, it is no surprise that industry leaders would be looking for a truly viable solution.
Why Predictive Maintenance?
Popularly called Industry 4.0, we are in the midst of what is being recognized as a the fourth industrial revolution. Predictive maintenance harnesses the power of the internet of things (IoT) to completely transform how things are made. In the past, managing an equipment lifecycle meant running the equipment until it fails, preventative maintenance, or taking a guess as to when an intervention might prevent failure. Now, with predictive maintenance, manufacturers are not left to making educated guesses. Instead, they monitor the continuous performance of the machine under normal conditions and look for subtle differences that may not have been noticeable through traditional inspections. These changes in performance serve as indicators of where a problem will occur. Operators and maintenance providers are able to now better plain their downtime, order parts, and minimize disruption. These steps have been known to reduce downtime by up to 50% and reduce maintenance costs by 30-40%.
Predictive maintenance tools cover a range of categories and work together to provide analytical data and keep industrial and manufacturing machines operating at their full potential. Here is a closer look at some of the benefits of predictive maintenance in the manufacturing sector:
IoT has enabled manufacturers to collect and monitor data on their equipment and machinery in real time, preventing lengthy, and costly, stoppages or delays. Manufacturers are able to use measurable data gained from IoT sensors to track the conditions of their machinery. The tools used to analyse the data make use of connectivity, advanced algorithms, and machine learning to identify the problems and offering the opportunity for preventative solutions. By allowing for the easy comparison of newly collected data with previously collected relevant data sets, companies are able to get a deeper understanding of their machines and a better sense of when failures may be on the horizon. Over time, as more data is collected, companies will be able to go deeper in their analytics and make better decisions. These improvements will lead to productivity gains and increased revenues by connecting all elements of manufacturing and creating better communication between machines and the people operating them.
Knowing and understanding the exact origin and history of the parts being used in manufacturing is essential to scheduling maintenance. Using IoT blockchain technologies, companies are able to track every component part of their machines. Companies like Boeing, have been using this technology to assess each part of their planes, giving the company, the government regulators, and maintenance personnel, a better understanding of the components and improving the efficiency of their maintenance efforts.
Improved Asset Management
Asset management is key to reducing downtime, preventing failures, improving efficiency and reducing production cycle times. One way manufacturers are improving these factors is through the adoption of digital twin technologies. Pioneered by NASA, digital twin technologies pairs physical and digital objects. Companies like General Electric have been using digital twins to improve their maintenance efforts.
The system collects operational and environmental data from the physical machine, like temperature and vibrations, showing users any potential problems that may arise. From there, the digital twin will assesses historical data and run a variety of simulations to determine which actions will be optimal. The machine will then perform the required actions either manually or through a downloaded app.This eliminates guesswork and allows manufacturers to use their assets to their full potential.
Predictive maintenance is currently a $3 billion industry but by 2022, it is expected to grow to $11 billion. These predictions alone make it obvious that predictive maintenance has the power to completely transform the manufacturing sector. Not only will it reduce costs, mechanical failures, and downtime, it is also likely to lead to innovations in equipment design. When manufacturers are better able to understand how and when their equipment fails or begins to deteriorate, they can work on creating solutions that avoid these pitfalls. There is no doubt that predictive maintenance is having an impact, and all the remains to be determined is just how wide ranging this impact will be.
Market Research Team