With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …

Purpose This article aims to systematically review the recent research advances in data-
driven machinery fault diagnosis based on machine learning algorithms, and provide …

Due to the limitations of data quality and quantity of a single industrial user, the development
of intelligent machinery fault diagnosis methods has been reaching a bottleneck in the …

Data-driven approaches for prognostic and health management (PHM) increasingly rely on
massive historical data, yet annotations are expensive and time-consuming. Learning …

Fault detection and diagnosis of gear systems using vibration measurements play an
important role in ensuring their functional reliability and safety. Computational intelligence …

Deep transfer learning algorithm is regarded as a promising method to address the issue of
rolling bearing fault diagnosis with limited labeled data. Stacked autoencoder (SAE) has …

Vibration signals are utilized widely for machinery fault diagnosis. These typical deep neural
networks (DNNs), eg, convolutional neural networks (CNNs) perform well in feature learning …

Intelligent fault diagnosis based on deep learning has been more attractive in practical
engineering. However, its accuracy is constrained by unlabeled data and large domain shift …

Recently, convolutional neural networks (CNNs) have achieved remarkable success in
machinery fault diagnosis. However, these methods usually require mass of manually …

Abstract The automation of Fault Detection and Diagnosis (FDD) is a central task for many
industries today. A myriad of methods are in use, although the most recent leading …