Application of Generative Autoencoders in the Detection of Anomalies in Hypercompressors

Abstract

Hypercompressors are essential assets that compress high-flow-rates of ethylene to pressures between 100~350 MPa in the LDPE industry. They are sources of essential risks and costs. This work proposes an unsupervised and univariate monitoring method for detecting anomalies in hypercompressors through data collected from an online monitoring system in an actual installation. A variational autoencoder learns the process of generating shapelets associated with vibrational patterns. A combination of matrix profile algorithms automatically selects the training data set. A β-VAE composed of MLP layers is trained and applied on the input space so that a voting operation and a box-cox transformation on the absolute residual errors between the inputs and outputs lead to the upper outlier detection threshold, obtained by the Tukey fence method. The model detected suspicious vibration patterns classified a priori as potential anomalies.