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Edge computing on IoT for machine signal processing and fault diagnosis: A review
Edge computing is an emerging paradigm that offloads the computations and analytics
workloads onto the Internet of Things (IoT) edge devices to accelerate the computation …
workloads onto the Internet of Things (IoT) edge devices to accelerate the computation …
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 …
Rotating machinery fault diagnosis under time-varying speeds: A review
D Liu, L Cui, H Wang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …
and harsh environments make its key parts, such as rolling bearings and gears, prone to …
An improved quantum-inspired differential evolution algorithm for deep belief network
W Deng, H Liu, J Xu, H Zhao… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Deep belief network (DBN) is one of the most representative deep learning models.
However, it has a disadvantage that the network structure and parameters are basically …
However, it has a disadvantage that the network structure and parameters are basically …
Rolling bearing fault diagnosis under data imbalance and variable speed based on adaptive clustering weighted oversampling
S Li, Y Peng, Y Shen, S Zhao, H Shao, G Bin… - Reliability Engineering & …, 2024 - Elsevier
Rolling bearings are critical for maintaining the stability, reliability, and safety of mechanical
systems. However, diagnosing faults in rolling bearings objectively can be challenging due …
systems. However, diagnosing faults in rolling bearings objectively can be challenging due …
Compound fault diagnosis for rotating machinery: State-of-the-art, challenges, and opportunities
Compound fault, as a primary failure leading to unexpected downtime of rotating machinery,
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
Modified stacked autoencoder using adaptive Morlet wavelet for intelligent fault diagnosis of rotating machinery
Intelligent fault diagnosis techniques play an important role in improving the abilities of
automated monitoring, inference, and decision making for the repair and maintenance of …
automated monitoring, inference, and decision making for the repair and maintenance of …
Few-shot GAN: Improving the performance of intelligent fault diagnosis in severe data imbalance
In severe data imbalance scenarios, fault samples are generally scarce, challenging the
health management of industrial machinery significantly. Generative adversarial network …
health management of industrial machinery significantly. Generative adversarial network …
Macroscopic–microscopic attention in LSTM networks based on fusion features for gear remaining life prediction
In the mechanical transmission system, the gear is one of the most widely used transmission
components. The failure of the gear will cause serious accidents and huge economic loss …
components. The failure of the gear will cause serious accidents and huge economic loss …
Few-shot learning under domain shift: Attentional contrastive calibrated transformer of time series for fault diagnosis under sharp speed variation
The domain shift of sample distribution caused by sharp speed variation dissatisfies the
general assumption of stationary conditions, which renders a severe challenge for a majority …
general assumption of stationary conditions, which renders a severe challenge for a majority …