Deep learning has proven to show superior performance in certain areas such as object recognition and image classification. It has also gained popularity in other domains such as finance where time-series data plays an important role. Similarly, in predictive maintenance, the data is collected over time to monitor the state of an asset with the goal of finding patterns to predict failures which can benefit from certain deep learning algorithms.
Among the deep learning networks, Long Short Term Memory (LSTM) networks are especially appealing to the predictive maintenance domain since they are very good at learning from sequences. This fact lends itself to their applications using time series data by making it possible to look back for longer periods of time to detect failure patterns.