It's a timeless manufacturing goal: to produce high quality products at a minimum cost. Factory 4.0 is already demonstrating its value by enabling manufacturers to reach this goal more successfully than ever, and one of the core technologies driving this new wave of ultra-automation is Industrial AI and Machine Learning. Data has become a valuable resource, and it's cheaper than ever to capture and store. Through the use of artificial intelligence, specifically Machine Learning, manufacturers can use data to significantly impact their bottom line by greatly improving efficiency, employee safety, and product quality. Maintenance represents a significant part of any manufacturing operation's expenses.
This blog post has been written with the collaboration of Juan Olloniego and Germán Hoffman. Even if machines have done a big part of the heavy lifting for us since the industrial revolution, they still depend on us for their maintenance. As they have that annoying tendency to break from time to time, their conservation becomes essential to keep up with our daily activities. Now, with the industry 4.0, the internet of things, and the artificial intelligence advent, we are letting a new kind of machines take care of their older counterparts. We make these new transistor-based machines look after their ancestors.
With advances in Artificial Intelligence and Machine Learning, traditional SCADA and manual statistical modelling are likely to be replaced. Advanced Statistical Modelling based on offline data is resource intensive and ultimately cannot provide real-time analytics that are actionable. As Automated Machine Learning gains traction, we expect more industrial plants to rely on this solution for Predictive Maintenance.
Think about all the machines you use during a year, all of them, from a toaster every morning to an airplane every summer holiday. Now imagine that, from now on, one of them would fail every day. What impact would that have? The truth is that we are surrounded by machines that make our life easier, but we also get more and more dependent on them. Therefore, the quality of a machine is not only based on how useful and efficient it is, but also on how reliable it is.
If you think carefully, you'll realize that the world we live in today is dependent heavily on the functioning of machines and systems. Almost everything from a light switch to a smartphone, from an elevator to a car, is a machine or a system that controls a machine. However, any machine is subject to wear and tear. What happens to a life so dependent on machines, when that particular machine breaks down? This is precisely why there's a dire need for predictive maintenance with machine learning.