… of unsupervised feature learning, we present a new framework of intelligent fault diagnosis,
as … two-stage learning method for intelligent fault diagnosis of machines. The illustration and …

… is a great challenge for rotating machinery fault diagnosis. In this paper, a novel deep
autoencoder feature learning method is developed to diagnose rotating machinery fault. Firstly, the …

… framework, built on spatiotemporal pattern network (STPN) [30], [31], to process multiple
time… learn spatiotemporal features. The learnt features are then connected to a deep learning …

… window by which the system observes its world, deep learning for fault diagnosis is put … the
accuracy of detection, classification and prediction, and efficient for incipient faults that cannot …

… Methodologies The GDBM is applied as a deep statistical feature learning tool for fault
diagnosis in this paper. The methodologies used are introduced in this section. In Section 2.1, …

… The high-precision fault diagnosis performance in this paper has proven that CNN has
strong feature learning ability. Therefore, the diagnostic accuracy can be guaranteed as long as …

… This section covers the experimental setup used to validate the deep learning based bearing
fault diagnostic technique. Fig. 5 shows the bearing test rig used to collect the AE data and …

… However, the CDFL approach presented in this paper is novel in the field of machinery
fault diagnosis and is able to learn invariant features based on the convolution and pooling …

… performance of deep learning comes with challenges and costs. This paper presents a
review of deep learning challenges related to machinery fault detection and diagnosis systems. …

… Deep learning aims to learn the general … learning essential features from raw data [27]. This
capability promotes the application of deep learning in the field of mechanical fault diagnosis …