I am trying to build a supervised ML-Model in the area of predictive Maintenance where the features are signal events, which can be represented with a gantt chart:
So my feature data consists of events with
signal_code (each row in the gantt chart represents all events of a certain signal code),
status (represented as the color in the chart). There can be up to a few hundred different signal codes.
I want to predict the likelyhood of an event (e.g. dark red in chart) say 30 minutes before it occures.
How could I detect "malicious" patterns prior to those events? Are there existing best practices for a use case like that?
I could find a lot information on time series (related question) but not for this kind of data. Obviously i will ne to apply some dimensionality reduction techniques such as PCA or LDA, but what will a do beforehand in order to put the data into a one-dimensional representation?
I welcome any advice on which algorithms or feature extraction methods might help! :)