Bearing fault diagnosis via generalized logarithm sparse regularization
Bearing fault is the most common causes of rotating machinery failure. Therefore, accurate
bearing fault identification technique is of tremendous significance. Vibration monitoring has …
bearing fault identification technique is of tremendous significance. Vibration monitoring has …
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 …
Central frequency mode decomposition and its applications to the fault diagnosis of rotating machines
To overcome current challenges in variational mode decomposition (VMD) and its variants
for the fault diagnosis of rotating machines, the decomposing characteristics of two sub …
for the fault diagnosis of rotating machines, the decomposing characteristics of two sub …
Wind turbine gearbox failure detection based on SCADA data: A deep learning-based approach
Gearbox failure is one of top-ranked factors leading to the unavailability of wind turbines
(WTs). Existing data-driven studies of gearbox failure detection (GFD) focus on improving …
(WTs). Existing data-driven studies of gearbox failure detection (GFD) focus on improving …
Multi-source fidelity sparse representation via convex optimization for gearbox compound fault diagnosis
Industrial automatic control systems have high requirements for manufacturing accuracy,
which are often adversely affected by the compound fault of rotating machinery such as …
which are often adversely affected by the compound fault of rotating machinery such as …
Graph-based semi-supervised random forest for rotating machinery gearbox fault diagnosis
S Chen, R Yang, M Zhong - Control Engineering Practice, 2021 - Elsevier
Random forest (RF) is an effective method for diagnosing faults of rotating machinery.
However, the diagnosis accuracy enhancement under insufficient labeled samples is still …
However, the diagnosis accuracy enhancement under insufficient labeled samples is still …
Coupling fault diagnosis of wind turbine gearbox based on multitask parallel convolutional neural networks with overall information
With the development of smart grid, capacity of wind power that connects to the grid
increases gradually, which makes the continuous and stable operation of wind turbine (WT) …
increases gradually, which makes the continuous and stable operation of wind turbine (WT) …
Machinery health monitoring based on unsupervised feature learning via generative adversarial networks
It confronts great difficulty to apply traditional artificial intelligence (AI) techniques to
machinery prognostics and health management in manufacturing systems due to the lack of …
machinery prognostics and health management in manufacturing systems due to the lack of …
Morphological component analysis under non-convex smoothing penalty framework for gearbox fault diagnosis
The sparse representation methodology has been identified to be a promising tool for
gearbox fault diagnosis. The core is how to precisely reconstruct the fault signal from noisy …
gearbox fault diagnosis. The core is how to precisely reconstruct the fault signal from noisy …
A novel impact feature extraction method based on EMD and sparse decomposition for gear local fault diagnosis
Sparse decomposition has been widely used in gear local fault diagnosis due to its
outstanding performance in feature extraction. The extraction results depend heavily on the …
outstanding performance in feature extraction. The extraction results depend heavily on the …