A survey of predictive maintenance: Systems, purposes and approaches
This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …
with emphasis on system architectures, purposes and approaches. In industry, any outages …
Vibration signal-based early fault prognosis: Status quo and applications
Abstract To implement Prognostics and Health Management (PHM) for industrial systems, it
is paramount to conduct early fault prognosis on the systems to ensure the stability and …
is paramount to conduct early fault prognosis on the systems to ensure the stability and …
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …
tremendous progress, which can help reduce costly breakdowns. However, different …
Partial transfer learning of multidiscriminator deep weighted adversarial network in cross-machine fault diagnosis
Z Wang, J Cui, W Cai, Y Li - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep transfer learning provides a feasible fault diagnosis method for intelligent mechanical
systems. However, this method usually assumes that the source domain and the target …
systems. However, this method usually assumes that the source domain and the target …
A novel deep clustering network using multi-representation autoencoder and adversarial learning for large cross-domain fault diagnosis of rolling bearings
H Wen, W Guo, X Li - Expert Systems with Applications, 2023 - Elsevier
Intelligent fault diagnosis based on deep learning has been more attractive in practical
engineering. However, its accuracy is constrained by unlabeled data and large domain shift …
engineering. However, its accuracy is constrained by unlabeled data and large domain shift …
A new structured domain adversarial neural network for transfer fault diagnosis of rolling bearings under different working conditions
This article presents a new deep transfer learning method, named structured domain
adversarial neural network (SDANN), for bearing fault diagnosis with the data collected …
adversarial neural network (SDANN), for bearing fault diagnosis with the data collected …
Attention-based convolutional denoising autoencoder for two-lead ECG denoising and arrhythmia classification
P Singh, A Sharma - IEEE Transactions on Instrumentation and …, 2022 - ieeexplore.ieee.org
This article presents a fast and accurate electrocardiogram (ECG) denoising and
classification method for low-quality ECG signals. To achieve this, a novel attention-based …
classification method for low-quality ECG signals. To achieve this, a novel attention-based …
Online detection for bearing incipient fault based on deep transfer learning
In order to achieve effective online detection of bearing incipient fault, it's necessary to
adaptively extract representative features to incipient fault. However, the traditional feature …
adaptively extract representative features to incipient fault. However, the traditional feature …
Digital Twin for rolling bearings: a review of current simulation and PHM techniques
Digital Twin (DT) is acknowledged as a promising technology for life-cycle management of
industrial products. Rolling bearings, the joints of machines, are largely responsible for the …
industrial products. Rolling bearings, the joints of machines, are largely responsible for the …
Health indicator based on signal probability distribution measures for machinery condition monitoring
Health indicator (HI), which aims to make quantitative measures for machinery operating
state at different degradation stages, is very critical in machinery condition monitoring. Some …
state at different degradation stages, is very critical in machinery condition monitoring. Some …