Transfer learning algorithms for bearing remaining useful life prediction: A comprehensive review from an industrial application perspective
Accurate remaining useful life (RUL) prediction for rolling bearings encounters many
challenges such as complex degradation processes, varying working conditions, and …
challenges such as complex degradation processes, varying working conditions, and …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
A systematic overview of health indicator construction methods for rotating machinery
Rotating machinery plays a vital role in the industrial sector, and ensuring its health status is
crucial for operational efficiency and safety. The construction of accurate health indicators …
crucial for operational efficiency and safety. The construction of accurate health indicators …
Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE
Prognostics and health management (PHM) is a critical work to ensure the reliable operation
of industrial equipment, in which health status (HS) assessment and remaining useful life …
of industrial equipment, in which health status (HS) assessment and remaining useful life …
Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression
Remaining useful life (RUL) prediction based on multimodal sensing data is indispensable
for predictive maintenance of aero-engine under cross-working conditions. Although data …
for predictive maintenance of aero-engine under cross-working conditions. Although data …
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 …
An interpretable deep transfer learning-based remaining useful life prediction approach for bearings with selective degradation knowledge fusion
This article tries to answer the two questions of bearings' remaining useful life (RUL)
prediction with deep transfer learning: what bearing data in the source domain contribute …
prediction with deep transfer learning: what bearing data in the source domain contribute …
Health indicator construction for degradation assessment by embedded LSTM–CNN autoencoder and growing self-organized map
Z Chen, H Zhu, J Wu, L Fan - Knowledge-Based Systems, 2022 - Elsevier
Health indicator (HI) construction is the most significant task of degradation assessment (DA)
that facilitates prognostic and health management of rotating machinery. Many stacked …
that facilitates prognostic and health management of rotating machinery. Many stacked …
Remaining useful life prediction of rolling bearings based on risk assessment and degradation state coefficient
Q Li, C Yan, G Chen, H Wang, H Li, L Wu - ISA transactions, 2022 - Elsevier
Abstract Prediction of Remaining Useful Life (RUL) of bearings is very important for the
condition-based maintenance of the rotating machinery. In order to predict the RUL more …
condition-based maintenance of the rotating machinery. In order to predict the RUL more …
Adversarial deep transfer learning in fault diagnosis: progress, challenges, and future prospects
Y Guo, J Zhang, B Sun, Y Wang - Sensors, 2023 - mdpi.com
Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the
superiorities of deep learning in feature representation with the merits of transfer learning in …
superiorities of deep learning in feature representation with the merits of transfer learning in …