[PDF][PDF] Deep unsupervised domain adaptation: A review of recent advances and perspectives
Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …
domains, partly because of its ability to learn from data and achieve impressive performance …
Data-driven remaining useful life estimation for milling process: sensors, algorithms, datasets, and future directions
An increase in unplanned downtime of machines disrupts and degrades the industrial
business, which results in substantial credibility damage and monetary loss. The cutting tool …
business, which results in substantial credibility damage and monetary loss. The cutting tool …
A survey of transfer learning for machinery diagnostics and prognostics
In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …
components greatly influence operational safety and system reliability. Many data-driven …
Bayesian transfer learning with active querying for intelligent cross-machine fault prognosis under limited data
Most existing deep learning (DL)-based health prognostic methods assume that the training
and testing datasets are from identical machines operating under similar conditions …
and testing datasets are from identical machines operating under similar conditions …
Bi-LSTM-based two-stream network for machine remaining useful life prediction
In industry, prognostics and health management (PHM) is used to improve the system
reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in …
reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in …
A new supervised multi-head self-attention autoencoder for health indicator construction and similarity-based machinery RUL prediction
Remaining useful life (RUL) prediction plays a significant role in the prognostic and health
management (PHM) of rotating machineries. A good health indicator (HI) can ensure the …
management (PHM) of rotating machineries. A good health indicator (HI) can ensure the …
Remaining useful life prediction of bearings under different working conditions using a deep feature disentanglement based transfer learning method
T Hu, Y Guo, L Gu, Y Zhou, Z Zhang, Z Zhou - Reliability Engineering & …, 2022 - Elsevier
The data distribution discrepancy between the training and test samples makes it
challenging for the remaining useful life (RUL) prediction under different working conditions …
challenging for the remaining useful life (RUL) prediction under different working conditions …
Dynamic model-assisted bearing remaining useful life prediction using the cross-domain transformer network
Remaining useful life (RUL) prediction of rolling bearings is of paramount importance to
various industrial applications. Recently, intelligent data-driven RUL prediction methods …
various industrial applications. Recently, intelligent data-driven RUL prediction methods …
Artificial intelligence enhanced fault prediction with industrial incomplete information
With the rapid advancement of sensor and information technology, fault prediction for
industrial equipment has become increasingly feasible. However, accurate fault prediction is …
industrial equipment has become increasingly feasible. However, accurate fault prediction is …
The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data
H Cheng, X Kong, Q Wang, H Ma, S Yang - Reliability Engineering & …, 2022 - Elsevier
The remaining useful life (RUL) prediction provides an essential basis for improving
mechanical equipment reliability. In practical application, the variant of working conditions …
mechanical equipment reliability. In practical application, the variant of working conditions …