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 …
Construction of health indicators for condition monitoring of rotating machinery: A review of the research
The condition monitoring (CM) of rotating machinery (RM) is an essential operation for
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …
improving the reliability of mechanical systems. For this purpose, an efficient CM method that …
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 …
Practical framework of Gini index in the application of machinery fault feature extraction
Gini index (GI) is an outstanding sparsity index that has high robustness for the interference
of the random impulse noise. Yet, as a new index, the definition of GI in different domains is …
of the random impulse noise. Yet, as a new index, the definition of GI in different domains is …
Fully interpretable neural network for locating resonance frequency bands for machine condition monitoring
In recent years, various neural networks have been developed to process vibration signals
for machine condition monitoring. Nevertheless, the physical interpretation of neural …
for machine condition monitoring. Nevertheless, the physical interpretation of neural …
Deep network-based maximum correlated kurtosis deconvolution: A novel deep deconvolution for bearing fault diagnosis
Deconvolution methods (DMs) which can adaptively design the filter for the feature
extraction is the most effective tool to counteract the effect of the transmission path …
extraction is the most effective tool to counteract the effect of the transmission path …
Fast nonlinear blind deconvolution for rotating machinery fault diagnosis
Z Zhang, J Wang, S Li, B Han, X Jiang - Mechanical Systems and Signal …, 2023 - Elsevier
Sparse optimization based early fault diagnosis method is drawing more and more attention.
In these methods, the objective function is usually a sparsity measure which can represent …
In these methods, the objective function is usually a sparsity measure which can represent …
Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum
The diagnosis of early-stage defects of rolling element bearings (REBs) using vibration
signals is a very difficult task since bearing fault signals are usually weak and masked by …
signals is a very difficult task since bearing fault signals are usually weak and masked by …
Fault diagnosis of rotating machines based on the EMD manifold
One challenge of the existing noise-assisted methods for solution of mode mixing problem of
empirical mode decomposition (EMD) is that, the decomposed modes contain much residual …
empirical mode decomposition (EMD) is that, the decomposed modes contain much residual …
Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network
Due to the complex and rugged working environment of real machinery equipment, the
resulting fault information is easily submerged by severe noise interference. Additionally …
resulting fault information is easily submerged by severe noise interference. Additionally …