We have talked about it for years now. Consultancies have been saying for a long time that we need it. Maybe you are even aiming towards it in your strategy? What exactly is Big Data? and how did we get to talk so much about it? Before reading further, try answering the following question - no cheating: How many V's defines Big Data?
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 .
There are many myths regarding Artificial Intelligence (AI) and Machine Learning (ML). As with other new technologies some are untrue and some are not. The myths that are untrue often create noise impairing us from making the right decisions about this new technology. In this blog post we will try to myth bust some of the most common myths within AI.
When we help customers use their data by utilizing machine learning we sometimes get asked “why is re-training important?”. This is natural as in software development when the code works you should let it be, if it aint broke don't fix it?
In this blog post we are talking about something that in machine learning is called data leakage. Please, do not misunderstand it as the leakage of data to the public. Data leakage in machine learning is when using a feature for predicting the output, that at the time of prediction cannot be available. In many cases, the feature holds information about the value we are trying to predict.
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