2023
DOI: 10.1109/tim.2022.3227956
|View full text |Cite
|
Sign up to set email alerts
|

A Parallel Hybrid Neural Network With Integration of Spatial and Temporal Features for Remaining Useful Life Prediction in Prognostics

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
26
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 47 publications
(26 citation statements)
references
References 53 publications
0
26
0
Order By: Relevance
“…The outcomes were measured in terms of achievement and evaluated via the national standardization test while taking into account some significant and influencing factors, such as lower SES, language barriers, cultural underground, and lack of permanent residence status. Jiusi Zhang et al [27] proposed a novel parallel hybrid model that is based on a 1D CNN and a bidirectional gated recurrent unit for the prediction of the remaining useful life and attained promising results using two datasets, i.e., the aircraft turbofan engine dataset and the milling dataset. In another study [28], the authors suggested a new method for predicting the remaining useful life, called VLSTM-LWSAN.…”
Section: Related Workmentioning
confidence: 99%
“…The outcomes were measured in terms of achievement and evaluated via the national standardization test while taking into account some significant and influencing factors, such as lower SES, language barriers, cultural underground, and lack of permanent residence status. Jiusi Zhang et al [27] proposed a novel parallel hybrid model that is based on a 1D CNN and a bidirectional gated recurrent unit for the prediction of the remaining useful life and attained promising results using two datasets, i.e., the aircraft turbofan engine dataset and the milling dataset. In another study [28], the authors suggested a new method for predicting the remaining useful life, called VLSTM-LWSAN.…”
Section: Related Workmentioning
confidence: 99%
“…In [ 19 ], data of high quality are generated by convolutional recurrent generative adversary networks to compensate for the shortage of data. Considering the parallel integration of the spatial and temporal features, ref [ 20 ] proposes a parallel hybrid neural network for the RUL prediction based on multi-sensor data. In engineering application, acquiring sufficient data may be difficult.…”
Section: Introductionmentioning
confidence: 99%
“…These DL algorithms have shown promising results in detecting and diagnosing electric motor faults (Neupane and Seok, 2020; Saufi et al, 2019). New methods have been introduced for fault diagnosis and prognosis, including the use of bidirectional gated recurrent units with temporal self-attention mechanisms for remaining useful life prediction (Zhang et al, 2023a). Another innovative approach involves an integrated multitasking intelligent bearing fault diagnosis scheme based on representation learning in imbalanced sample conditions (Zhang et al, 2023b).…”
Section: Introductionmentioning
confidence: 99%