[HTML][HTML] Deep learning techniques in intelligent fault diagnosis and prognosis for industrial systems: A review
S Qiu, X Cui, Z Ping, N Shan, Z Li, X Bao, X Xu - Sensors, 2023 - mdpi.com
Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …
captured sensory data, and also predict their failures in advance, which can greatly help to …
Battery prognostics and health management from a machine learning perspective
Transportation electrification is gaining prominence as a significant pathway for reducing
emissions and enhancing environmental sustainability. Central to this shift are lithium-ion …
emissions and enhancing environmental sustainability. Central to this shift are lithium-ion …
An integrated multi-head dual sparse self-attention network for remaining useful life prediction
Committed to accident prevention, prediction of remaining useful life (RUL) plays a crucial
role in prognostics health management technology. Conventional convolutional neural …
role in prognostics health management technology. Conventional convolutional neural …
A parallel hybrid neural network with integration of spatial and temporal features for remaining useful life prediction in prognostics
Prediction of remaining useful life (RUL) is an indispensable part of prognostics health
management (PHM) in complex systems. Considering the parallel integration of the spatial …
management (PHM) in complex systems. Considering the parallel integration of the spatial …
A new convolutional dual-channel Transformer network with time window concatenation for remaining useful life prediction of rolling bearings
L Jiang, T Zhang, W Lei, K Zhuang, Y Li - Advanced Engineering …, 2023 - Elsevier
Deep learning has achieved numerous breakthroughs in bearing predicting remaining
useful life (RUL). However, the current mainstream deep learning framework inevitably has …
useful life (RUL). However, the current mainstream deep learning framework inevitably has …
Trans-Lighter: A light-weight federated learning-based architecture for Remaining Useful Lifetime prediction
Predictive maintenance (PdM) plays an important role in industrial manufacturing. One of the
most fundamental ideas underlying many PdM solutions is to estimate Remaining Useful …
most fundamental ideas underlying many PdM solutions is to estimate Remaining Useful …
Trend-augmented and temporal-featured Transformer network with multi-sensor signals for remaining useful life prediction
Y Zhang, C Su, J Wu, H Liu, M Xie - Reliability Engineering & System Safety, 2024 - Elsevier
Deep learning method has obtained abundant achievements in remaining useful life (RUL)
prediction, which can steer the preventive maintenance decision-making for improving the …
prediction, which can steer the preventive maintenance decision-making for improving the …
Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks
Battery-powered electric vehicles (EVs) are poised to accelerate decarbonization in nearly
every aspect of transportation. However, safety issues of commercial lithium-ion batteries …
every aspect of transportation. However, safety issues of commercial lithium-ion batteries …
DLformer: A dynamic length transformer-based network for efficient feature representation in remaining useful life prediction
Representation learning-based remaining useful life (RUL) prediction plays a crucial role in
improving the security and reducing the maintenance cost of complex systems. Despite the …
improving the security and reducing the maintenance cost of complex systems. Despite the …
A lightweight and adaptive knowledge distillation framework for remaining useful life prediction
For prognostics and health management of industrial systems, machine remaining useful life
(RUL) prediction is an essential task. While deep learning-based methods have achieved …
(RUL) prediction is an essential task. While deep learning-based methods have achieved …