Digitization is a word that is mentioned in most meeting rooms. We have met many companies that have a 2025 goal of digitizing parts of their business. But what exactly is digitization? What are the benefits of becoming more digital? When is it satisfying and how can one kickstart the process? Is it important to your business at all?
In this blog post, we will discuss why you should stop focusing on Big Data and especially the big part, which is often interpreted as the number of observations we have in a dataset.
In a previous blog post we showed how Machine Learning can estimate the Remaining Useful Life of a turbofan engine.
In this blog post, we will put our internal library, Cohen to the test, by estimating the Remaining Useful Life on NASA’s Turbofan engine with six different conditions (0 to 20,000 feet), and one fault mode, HPC Degradation.
Afterwards, we will test it when having two fault modes: HPC Degradation and Fan Degradation.
We have previously given an introduction to supervised learning, but there is also unsupervised learning. This blog post will give you an introduction to unsupervised learning and why it might be smart to use for certain types of issues.
Should Predictive Maintenance be a standard add-on when you buy new machines or can it be added to equipment that you already have? We have previously written about what to look for in predictive maintenance sensors. In this blog post we will dive in depth with how you can retrofit sensors on the machines that you have in your production today and get started with predictive maintenance.
Within machine learning there is a learning form called supervised learning. But what does “supervised” mean? Are there any special considerations you need to make when doing supervised learning? Read on and get a introduktion to supervised learning.
In this blog post, we will see how much better a machine learning model we can create by having more breakdowns available for training. Thus, we have split the dataset in to 7, 30, and 100 breakdowns and will see how much better a model gets, when having a larger representation of the wear and tear degradation.