Small data challenges for intelligent prognostics and health management: a review
Prognostics and health management (PHM) is critical for enhancing equipment reliability
and reducing maintenance costs, and research on intelligent PHM has made significant …
and reducing maintenance costs, and research on intelligent PHM has made significant …
A review on deep learning in planetary gearbox health state recognition: Methods, applications, and dataset publication
D Liu, L Cui, W Cheng - Measurement Science and Technology, 2023 - iopscience.iop.org
Planetary gearboxes have various merits in mechanical transmission, but their complex
structure and intricate operation modes bring large challenges in terms of fault diagnosis …
structure and intricate operation modes bring large challenges in terms of fault diagnosis …
Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine
Wind turbines play a crucial role in renewable energy generation systems and are frequently
exposed to challenging operational environments. Monitoring and diagnosing potential …
exposed to challenging operational environments. Monitoring and diagnosing potential …
[HTML][HTML] Physics-informed machine learning: a comprehensive review on applications in anomaly detection and condition monitoring
Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …
various engineering systems. Traditional methods for condition monitoring rely on physics …
[HTML][HTML] Semi-supervised learning for industrial fault detection and diagnosis: A systemic review
JM Ramírez-Sanz, JA Maestro-Prieto… - ISA transactions, 2023 - Elsevier
Abstract The automation of Fault Detection and Diagnosis (FDD) is a central task for many
industries today. A myriad of methods are in use, although the most recent leading …
industries today. A myriad of methods are in use, although the most recent leading …
A holistic semi-supervised method for imbalanced fault diagnosis of rotational machinery with out-of-distribution samples
Fault diagnosis plays a critical role in ensuring the reliability and safety of industrial systems.
Despite the success of semi-supervised learning in fault diagnosis, challenges persist in …
Despite the success of semi-supervised learning in fault diagnosis, challenges persist in …
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 …
Classifier-guided neural blind deconvolution: A physics-informed denoising module for bearing fault diagnosis under noisy conditions
Blind deconvolution (BD) has been demonstrated to be an efficacious approach for
extracting bearing fault-specific features from vibration signals under strong background …
extracting bearing fault-specific features from vibration signals under strong background …
Transfer learning for prognostics and health management: Advances, challenges, and opportunities
As failure data is usually scarce in practice upon preventive maintenance strategy in
prognostics and health management (PHM) domain, transfer learning provides a …
prognostics and health management (PHM) domain, transfer learning provides a …
Physics-informed machine learning in prognostics and health management: State of the art and challenges
Prognostics and health management (PHM) plays a constructive role in the equipment's
entire life health service. It has long benefited from intensive research into physics modeling …
entire life health service. It has long benefited from intensive research into physics modeling …