Analysis of different RNN autoencoder variants for time series classification and machine prognostics

Recurrent neural network (RNN) based autoencoders, trained in an unsupervised manner, have been widely used to generate fixed-dimensional vector representations or embeddings for varying length multivariate time series. These embeddings have been demonstrated to be useful for time series reconstruction, classification, and creation of health index (HI) curves of machines being used in industrial applications, based on which the remaining useful …