Predictive maintenance of rotational machinery using deep learning

This paper describes an implementation of a deep learning-based predictive
maintenance (PdM) system for industrial rotational machinery, built upon
the foundation of a long short-term memory (LSTM) autoencoder and
regression analysis. The autoencoder identifies anomalous patterns, while
the latter, based on the autoencoder’s output, estimates the machine’s
remaining useful life (RUL). Unlike prior PdM systems dependent on
labelled historical data, the developed system doesn’t require it as it’s based
on an unsupervised deep learning model, enhancing its adaptability. The
paper also explores a robust condition monitoring system that collects
machine operational data, including vibration and current parameters, and
transmits them to a database via a Bluetooth low energy (BLE) network.
Additionally, the study demonstrates the integration of this PdM system
within a web-based framework, promoting its adoption across various
industrial settings. Tests confirm the system's ability to accurately identify
faults, highlighting its potential to reduce unexpected downtime and enhance
machinery reliability.