A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
Quo vadis artificial intelligence?
The study of artificial intelligence (AI) has been a continuous endeavor of scientists and
engineers for over 65 years. The simple contention is that human-created machines can do …
engineers for over 65 years. The simple contention is that human-created machines can do …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises
Anomaly detection of machine tools plays a vital role in the machinery industry to sustain
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …
Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective
Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …
challenges such as complex degradation processes, varying working conditions, and …
A review on transfer learning in EEG signal analysis
Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
interaction and neurological disease diagnosis, requires a large amount of labeled data for …
Intelligent fault diagnosis of rotor-bearing system under varying working conditions with modified transfer convolutional neural network and thermal images
The existing intelligent fault diagnosis methods of rotor-bearing system mainly focus on
vibration analysis under steady operation, which has low adaptability to new scenes. In this …
vibration analysis under steady operation, which has low adaptability to new scenes. In this …
A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges
V Nasir, F Sassani - The International Journal of Advanced Manufacturing …, 2021 - Springer
Data-driven methods provided smart manufacturing with unprecedented opportunities to
facilitate the transition toward Industry 4.0–based production. Machine learning and deep …
facilitate the transition toward Industry 4.0–based production. Machine learning and deep …
A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation
Predictions of renewable energy (RE) generation and electricity load are critical to smart grid
operation. However, the prediction task remains challenging due to the intermittent and …
operation. However, the prediction task remains challenging due to the intermittent and …
A systematic review of deep transfer learning for machinery fault diagnosis
With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
such intelligent computing methods as deep learning ones for machinery fault diagnosis …