Wavelet transform for rotary machine fault diagnosis: 10 years revisited
As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …
Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning
Deep learning models have revolutionized numerous fields, yet their decision-making
processes often remain opaque, earning them the characterization of “black-box” models …
processes often remain opaque, earning them the characterization of “black-box” models …
Explainability-driven model improvement for SOH estimation of lithium-ion battery
Deep neural networks have been widely used in battery health management, including state-
of-health (SOH) estimation and remaining useful life (RUL) prediction, with great success …
of-health (SOH) estimation and remaining useful life (RUL) prediction, with great success …
Deep continual transfer learning with dynamic weight aggregation for fault diagnosis of industrial streaming data under varying working conditions
Catastrophic forgetting of learned knowledges and distribution discrepancy of different data
are two key problems within fault diagnosis fields of rotating machinery. However, existing …
are two key problems within fault diagnosis fields of rotating machinery. However, existing …
[HTML][HTML] Intelligent fault diagnosis of helical gearboxes with compressive sensing based non-contact measurements
Helical gearboxes play a critical role in power transmission of industrial applications. They
are vulnerable to various faults due to long-term and heavy-duty operating conditions. To …
are vulnerable to various faults due to long-term and heavy-duty operating conditions. To …
Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions
In recent years, the rapid development of convolutional neural networks (CNNs) has
significantly advanced the progress of intelligent fault diagnosis. Most currently-available …
significantly advanced the progress of intelligent fault diagnosis. Most currently-available …
A domain feature decoupling network for rotating machinery fault diagnosis under unseen operating conditions
T Gao, J Yang, W Wang, X Fan - Reliability Engineering & System Safety, 2024 - Elsevier
Operating conditions reflect the mission evolution of rotating machinery in specific
application scenarios. The monitoring data under different operating conditions exhibit …
application scenarios. The monitoring data under different operating conditions exhibit …
Applications of deep learning for drug discovery systems with bigdata
Y Matsuzaka, R Yashiro - BioMedInformatics, 2022 - mdpi.com
The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process
of pharmaceutical research and development, is progressing. By using the ability to process …
of pharmaceutical research and development, is progressing. By using the ability to process …
Fault Diagnosis using eXplainable AI: A transfer learning-based approach for rotating machinery exploiting augmented synthetic data
Due to the growing interest for increasing productivity and cost reduction in industrial
environment, new techniques for monitoring rotating machinery are emerging. Artificial …
environment, new techniques for monitoring rotating machinery are emerging. Artificial …
Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion
X Zhang, Z Leng, Z Zhao, M Li, D Yu, X Chen - Advanced Engineering …, 2023 - Elsevier
Due to complex spatial correlations, dynamic temporal trends, and heterogeneities, accurate
remaining useful life (RUL) prediction is a challenging task for multi-sensor complex …
remaining useful life (RUL) prediction is a challenging task for multi-sensor complex …