Predictive Maintenance with LSTM Siamese Network
Detect Failures from Sensor Time Series
Predictive Maintenance nowadays is an active field where Artificial Intelligence solutions are introduced to improve the productivity of every kind of manufacturing system. Typical requests involve developing a solution that produces warnings when particular parts of observed components are closed to failure. As can be guessed, these are classic ‘situations of unbalance’ where we have tons of data coming from every kind of sensor but a lack of positive labeled samples, where a positive sample is the presence of a failure.
In a previous post, I’ve introduced a solution that classifies the degradation status of a particular component in a hydraulic pipeline system; the results were cool! Now, I try to develop a pipeline that detects if a storage system is close to failure. We want to classify continuous time series coming from sensors making use of Neural Networks. In particular, I avoid the problem of lack in failure samples using Siamese Networks adapted to fit time series with an LSTM architecture.
THE DATASET
As always, I prefer to work with real data. I found a fantastic dataset on Kaggle: High Storage System Data for Energy Optimization, released by Smartfactory. The dataset stores the signals of 4 short…