Currently, few samples and the inevitable noise poses a severe test on deep learning
methods. To solve the above problems, a novel fault diagnosis network based on a refined …

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 …

The faults of harmonic reducers result in excessive vibration affecting the joint stabilization of
industrial robots and manufacturing quality. In-situ fault diagnosis of harmonic reducers can …

The cross-machine diagnosis of CNC spindle motors with compound faults is essential and
challenging because of the subsystem coupling and individual difference. This paper …

Prognostics and health management (PHM) is an engineering approach dealing with the
diagnosis, prognosis, and management of the health state of engineering systems. Methods …

The faults from the spindle motor of CNC machines result in excessive vibration affecting the
manufacturing quality. In-situ signals of intact motors are complex and nonlinear due to …

This paper proposes a fault diagnosis method for multi-sensor vibration signals under few
computing resources, called multi-level feature fusion convolution neural network based on …

The diagnosis of rolling bearing faults has become an increasingly popular research topic in
recent years. However, many studies have been conducted based on sufficient training data …

Purpose The mechanical fault diagnosis method based on deep learning mainly uses single-
scale convolution kernels to extract fault features, which is difficult to extract fault feature …

Because of uncertain working conditions and the lack of labels for most conditions, transfer
learning has great potential in fault diagnosis for high-speed train bogies. This article …