Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks
In recent years, intelligent data-driven prognostic methods have been successfully
developed, and good machinery health assessment performance has been achieved …
developed, and good machinery health assessment performance has been achieved …
Industrial remaining useful life prediction by partial observation using deep learning with supervised attention
Effective and reliable machinery health assessment and prognostic methods have been
highly demanded in modern industries. In the past years, promising prognostic results have …
highly demanded in modern industries. In the past years, promising prognostic results have …
Data alignments in machinery remaining useful life prediction using deep adversarial neural networks
Recently, intelligent data-driven machinery prognostics and health management have been
attracting increasing attention due to the great merits of high accuracy, fast response and …
attracting increasing attention due to the great merits of high accuracy, fast response and …
Deep separable convolutional network for remaining useful life prediction of machinery
Deep learning is gaining attention in data-driven remaining useful life (RUL) prediction of
machinery because of its powerful representation learning ability. With the help of deep …
machinery because of its powerful representation learning ability. With the help of deep …
A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems
Remaining useful life (RUL) estimation plays a crucial role in evaluating health states and
improving maintenance plans of mechanical systems. Recently, artificial intelligence-based …
improving maintenance plans of mechanical systems. Recently, artificial intelligence-based …
A deep attention residual neural network-based remaining useful life prediction of machinery
F Zeng, Y Li, Y Jiang, G Song - Measurement, 2021 - Elsevier
Remaining useful life (RUL) estimation has always been an essential task of prognostics
health management (PHM). However, degradation data of machinery is seldom available …
health management (PHM). However, degradation data of machinery is seldom available …
Multiscale convolutional attention network for predicting remaining useful life of machinery
To integrate the complete degradation information of machinery, deep learning-based
prognostics approaches usually use monitoring data acquired by different sensors as the …
prognostics approaches usually use monitoring data acquired by different sensors as the …
Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes
Data-driven remaining useful life (RUL) prediction approaches, especially those based on
deep learning (DL), have been increasingly applied to mechanical equipment. However, two …
deep learning (DL), have been increasingly applied to mechanical equipment. However, two …
A deep learning framework for sensor-equipped machine health indicator construction and remaining useful life prediction
J Yan, Z He, S He - Computers & Industrial Engineering, 2022 - Elsevier
Prognostic and health management (PHM) effectively reduces the economic loss of sensor-
equipped machine downtime caused by under-maintenance and the waste of resources …
equipped machine downtime caused by under-maintenance and the waste of resources …
Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors
Many data-driven models normally assume that the training and test data are independent
and identically distributed to predict the remaining useful life (RUL) of industrial machines …
and identically distributed to predict the remaining useful life (RUL) of industrial machines …
相关搜索
- life prediction fault modes
- life prediction sensor malfunctions
- life prediction deep learning
- life prediction partial observation
- life prediction operating conditions
- life prediction supervised attention
- deep learning partial observation
- life estimation neural network
- life prediction deep attention
- life prediction working conditions