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 Machine Learning model gets, when having a larger representation of the wear and tear degradation.
Selecting the right sensors for predictive maintenance can be a jungle. This blog post will give you advices for what to look for and how you can get started on your journey. We will also take a look into common pitfalls and what you need to be aware of as a customer to get the right sensors which fits your production.
What is the P-F interval and why is it important when talking about predictive maintenance? In this blog post you will get an introduction to the P-F interval and how predictive maintenance with machine learning can improve the detection of potential failure and give you the possibility to plan maintenance before a failure happens.
Many address Machine Learning projects as black box solutions or product delivery.
However, Machine Learning projects delivered rightly is much more than that.
It gives you insight to whether you retrieve the right data, whether your hypotheses on patterns are true, and so much more.
In this blog post, we will talk about the benefits of using Remaining Useful Life predictions as a main driver for planning maintenance and detecting wear and tear before problems arise. There is a high potential of both reducing total costs on maintenance and spare parts, but also on increasing the total operational uptime, by using data when planning maintenance intervals.
In this blog post we will go through the different approaches to predictive maintenance. Right data is always the foundation and different types of machine learning algorithms can be used depending on the current situation and the problem/value we are aiming for.
According to the Danish Maintenance Association (DDV), machinery maintenance is estimated to cost Danish manufacturing companies DKK 25 billions each year. Additionally, it is estimated that there is a globally cost reduction of $240 - $630 billions in 2025, thanks to the implementation of predictive maintenance .
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