CFCNN: A novel convolutional fusion framework for collaborative fault identification of rotating machinery
Sensor techniques and emerging CNN models have greatly facilitated the development of
collaborative fault diagnosis. Existing CNN models apply different fusion schemes to …
collaborative fault diagnosis. Existing CNN models apply different fusion schemes to …
Fault diagnosis of planetary gears based on intrinsic feature extraction and deep transfer learning
The planetary gearbox is a key transmission apparatus used to change speed and torque.
The planetary gear is one of the most failure-prone components in a planetary gearbox. Due …
The planetary gear is one of the most failure-prone components in a planetary gearbox. Due …
A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet
Rolling bearings play a crucial role as components in mechanical equipment.
Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose …
Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose …
A novel intelligent multicross domain fault diagnosis of servo motor-bearing system based on Domain Generalized Graph Convolution Autoencoder
The data measured by the servo motor-bearing system under complex working conditions
will present problems such as amplitude fluctuations, unequal impact intervals, and …
will present problems such as amplitude fluctuations, unequal impact intervals, and …
Period-refined CYCBD using time synchronous averaging for the feature extraction of bearing fault under heavy noise
Y Miao, H Shi, C Li, J Hua, J Lin - Structural Health …, 2024 - journals.sagepub.com
Deconvolution methods have been widely used in machinery fault diagnosis. However, their
application would be confined due to the heavy noise and complex interference since the …
application would be confined due to the heavy noise and complex interference since the …
A novel multivariate cutting force-based tool wear monitoring method using one-dimensional convolutional neural network
Tool wear condition monitoring during the machining process is one of the most important
considerations in precision manufacturing. Cutting force is one of the signals that has been …
considerations in precision manufacturing. Cutting force is one of the signals that has been …
Broad Distributed Game Learning for intelligent classification in rolling bearing fault diagnosis
H Liu, H Pan, J Zheng, J Tong, M Zhu - Applied Soft Computing, 2024 - Elsevier
Abstract As a new Single Layer Feedforward Network (SLFN) architecture, Broad Learning
System (BLS) has been widely used in the field of fault diagnosis because of its fast-training …
System (BLS) has been widely used in the field of fault diagnosis because of its fast-training …
Amplitude-based multiscale reverse dispersion entropy: a novel approach to bearing fault diagnosis
The multiscale fluctuation dispersion entropy algorithm (MFDE) is widely used to extract the
characteristics from a variety of complex nonlinear signals, including bearing signals, due to …
characteristics from a variety of complex nonlinear signals, including bearing signals, due to …
Cubic spline interpolation-based refined composite multiscale dispersion entropy and its application to bearing fault identification
As a powerful tool, dispersion entropy (DE) has good capability to measure the irregularity
and complexity of nonlinear systems, so it is extensively utilized in the field of structural …
and complexity of nonlinear systems, so it is extensively utilized in the field of structural …
Wavelet sparsity enhancement for extracting transient vibration signatures of bearing structural damages
X Zhang, J Wang, L Wu, F Meng… - Structural Health …, 2023 - journals.sagepub.com
Wavelet methods are widely used in mechanical transient vibration signature detection and
fault diagnosis. Undesirable artifacts (eg, spurious noise spikes and pseudo-Gibbs …
fault diagnosis. Undesirable artifacts (eg, spurious noise spikes and pseudo-Gibbs …