Building a Predictive Maintenance Solution Using AWS AutoML and No-Code Tools
In this post, we describe how equipment operators can build a predictive maintenance solution using AutoML and no-code tools powered by Amazon Web Services (AWS). This type of solution delivers significant gains to large-scale industrial systems and mission-critical applications where costs associated with machine failure or unplanned downtime can be high.
To implement a prototype of the RUL model, we use a publicly available dataset known as NASA Turbofan Jet Engine Data Set. This dataset is often used for research and ML competitions. The dataset includes degradation trajectories of 100 turbofan engines obtained from a simulator. Here, we explore only one of the four sub-datasets included, namely the training part of the dataset: FD001.