Estimating the Remaining Useful Life on an Aircraft Engine

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.

Image of a jet engine

Estimating the Remaining Useful Life of a Water Pump

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.

Diagram showing the predicted and true RUL of the water pump

Using Talos for Feature Hyperparameter Optimization?

When designing the architecture for an artificial neural network, there exist a variety of parameters that can be tuned. It is indeed an art in itself to find the right combination for these parameters to achieve the highest accuracy and lowest loss. In this blog post, we are testing the usage of Talos for hyperparameter optimization of a neural network.

Loss and accuracy diagrams

Predicting Credit Card Fraud with Machine Learning

Credit card fraud is an ongoing problem in the bank industry. With the contactless payment possibility and e-commerce it is even easier to misuse another person's credit card.

Hand holding credit card