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 …
A review on the application of blind deconvolution in machinery fault diagnosis
Fault diagnosis is of significance for ensuring the safe and reliable operation of machinery
equipment. Due to the heavy noise and interference, it is difficult to detect the fault directly …
equipment. Due to the heavy noise and interference, it is difficult to detect the fault directly …
A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis
Being an effective methodology to adaptatively decompose a multi-component signal into a
series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited …
series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited …
A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions
The data-driven methods in machinery fault diagnosis have become increasingly popular in
the past two decades. However, the wide applications of this scheme are generally …
the past two decades. However, the wide applications of this scheme are generally …
Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution
Z Wang, J Zhou, W Du, Y Lei, J Wang - Mechanical Systems and Signal …, 2022 - Elsevier
Blind deconvolution has been proved to be an effective method for fault detection since it
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …
can recover periodic impulses from mixed fault signals convoluted by noise and periodic …
Feature mode decomposition: New decomposition theory for rotating machinery fault diagnosis
Y Miao, B Zhang, C Li, J Lin… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this article, a new decomposition theory, feature mode decomposition (FMD), is tailored
for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose …
for the feature extraction of machinery fault. The proposed FMD is essentially for the purpose …
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …
machines, balanced training data for different machine health conditions are assumed in …
Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions
Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of
practical importance. For this purpose, ensemble transfer convolutional neural networks …
practical importance. For this purpose, ensemble transfer convolutional neural networks …
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 …
ChatGPT-like large-scale foundation models for prognostics and health management: A survey and roadmaps
PHM technology is vital in industrial production and maintenance, identifying and predicting
potential equipment failures and damages. This enables proactive maintenance measures …
potential equipment failures and damages. This enables proactive maintenance measures …