Predictive maintenance revolutionizes the future of machines. It tracks each of the machines’ unique life cycle and doesn’t generalize. It allows us to know if they need to be attended to in advance. This maintenance method provides information on upcoming, unknown or unpredicted critical failures. It creates an effective and innovative environment.
Using data science to solve predictive maintenance revolutionizes the way we look at machines. It changes the data collection approach, enhances its quality and allows proper usage of the collected data. In a world full of machines, we need to be the bridge connecting the methods of the past to the opportunities of the future.
I am an R&D group lead at a big organization. One of the main projects we’re working on is predictive maintenance using data science tools. This comes as opposed to classical Mechanical Engineering models, which were the main solution in the field until very recently. I have seen this project end-to-end, and have looked into both Mechanical Engineering and data science various approaches. In my talk I will present both methods, and explain why data science revolutionizes the entire field of maintenance and changes decades long conceptions.