… the machinery fault diagnosis field and the merits of DL for overcoming several challenges.
The process for a diagnosis model based on conventional machine learning algorithms and …

… feature learning and fault classification. Therefore, in this review, DL and DL-based
intelligent fault diagnosis techniques are overviewed. DL-based fault diagnosis approaches for …

… on DL-based intelligent fault diagnosis for rotating machinery. Based on deep learning
models, this paper divides existing research into five categories: deep belief networks (DBN), …

… for intelligent fault diagnosis. It incorporates the processes of feature extraction and fault
recognition into a general-purpose learning … be used to directly learn features from raw vibration …

… deep learning (DL) models show promising results on intelligent fault detection applications
for machine … the data and their ability to learn features from measurement data automatically. …

… on utilising deep learning so as to build a diagnostic system … Deep SymptomsBased Model
Deep-SBM, as an approach to predict a wide range of faults by relying on the deep learning …

… the acquisition of fault sample labeled information is burdensome, yet costly. Naturally, the …
intelligent fault diagnosis method of rotating machinery based on unsupervised deep learning …

… for computing intelligent fault diagnosis [5]. In recent times, DL and Deep Neural Networks
(DNN) … attention among researchers and are used in mechanical fault diagnosing operations. …

… diagnose the various faults of aircraft key parts. As a very promising tool in the field of intelligent
fault diagnosis, deep learning … design and engineering diagnosis experience, which can …

… review of deep learning based intelligent industrial FDP. It … DBN, or RNN for fault diagnosis
purposes, but when architectures … deep learning methods have been applied in the intelligent …