LEFE-Net: A lightweight efficient feature extraction network with strong robustness for bearing fault diagnosis
High precision and fast fault diagnosis is an important guarantee for the safe and reliable
operation of machinery. In recent years, due to the strong recognition ability, data-driven …
operation of machinery. In recent years, due to the strong recognition ability, data-driven …
Deep residual network for identifying bearing fault location and fault severity concurrently
L Chen, G Xu, T Tao, Q Wu - IEEE Access, 2020 - ieeexplore.ieee.org
Fault diagnosis is composed of two tasks, ie, fault location detection and fault severity
identification, which are both significant to equipment maintenance. The former can indicate …
identification, which are both significant to equipment maintenance. The former can indicate …
Novelty detection and fault diagnosis method for bearing faults based on the hybrid deep autoencoder network
Y Zhao, H Hao, Y Chen, Y Zhang - Electronics, 2023 - mdpi.com
In the event of mechanical equipment failure, the fault may not belong to any known
category, and existing deep learning methods often misclassify such faults into a known …
category, and existing deep learning methods often misclassify such faults into a known …
Fault diagnosis for rolling bearings based on multiscale feature fusion deep residual networks
X Wu, H Shi, H Zhu - Electronics, 2023 - mdpi.com
Deep learning, due to its excellent feature-adaptive capture ability, has been widely utilized
in the fault diagnosis field. However, there are two common problems in deep-learning …
in the fault diagnosis field. However, there are two common problems in deep-learning …
A bearing fault diagnosis method without fault data in new working condition combined dynamic model with deep learning
K Xu, X Kong, Q Wang, S Yang, N Huang… - Advanced Engineering …, 2022 - Elsevier
Bearing fault diagnosis plays an important role in rotating machinery equipment's safe and
stable operation. However, the fault sample collected from the equipment is seriously …
stable operation. However, the fault sample collected from the equipment is seriously …
Enhanced discriminate feature learning deep residual CNN for multitask bearing fault diagnosis with information fusion
Deep learning-based diagnosis methods currently face some challenges and open
problems. First, domain knowledge of fault modes and operating conditions are not …
problems. First, domain knowledge of fault modes and operating conditions are not …
Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence
Intelligent condition monitoring and fault diagnosis by analyzing the sensor data can assure
the safety of machinery. Conventional fault diagnosis and classification methods usually …
the safety of machinery. Conventional fault diagnosis and classification methods usually …
A novel local binary temporal convolutional neural network for bearing fault diagnosis
In bearing fault diagnosis, the faulty data are generally limited due to the high cost of fault
signal collection. Considering the excessive parameters in the traditional convolutional …
signal collection. Considering the excessive parameters in the traditional convolutional …
Bearing fault diagnosis based on multiple transformation domain fusion and improved residual dense networks
J Sun, J Wen, C Yuan, Z Liu, Q Xiao - IEEE Sensors Journal, 2021 - ieeexplore.ieee.org
Automatic feature extraction is one of the most advantageous merits of deep neural network
(DNN), meanwhile, it is an important part for intelligent bearing fault diagnosis. However …
(DNN), meanwhile, it is an important part for intelligent bearing fault diagnosis. However …
A hybrid feature model and deep-learning-based bearing fault diagnosis
Bearing fault diagnosis is imperative for the maintenance, reliability, and durability of rotary
machines. It can reduce economical losses by eliminating unexpected downtime in industry …
machines. It can reduce economical losses by eliminating unexpected downtime in industry …