Engine remaining useful life prediction based on PSO optimized multi-layer long short-term memory and multi-source information fusion Article Swipe
Yuan Wei
,
Xinlong Li
,
Hongbin Gu
,
Faye Zhang
,
Fei Miao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1177/00202940231214868
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1177/00202940231214868
Engine as the core component of mechanical equipment, its operating state directly affects whether the equipment can operate normally. Predicting the engine remaining useful life (RUL) can monitor the health of the engine in real time and formulate a timely and reasonable maintenance plan. Aiming at the engine monitoring data with various and long time span, we propose a direct prediction method of engine RUL based on particle swarm optimization (PSO) optimized multi-layer Long Short-Term Memory (LSTM) in this paper. Firstly, the monitoring data that can well reflect the engine degradation trend is screened out, and the samples are constructed through a sliding time window. Then, a multi-layer LSTM model is constructed to mine the deep-seated features of the samples for predicting the engine RUL. Finally, the hyperparameters of the multi-layer LSTM model are optimized automatically by the PSO algorithm to optimize the performance of the model. The effectiveness of this method is verified by NASA data set. RMSE, MAE and the scoring function are used as evaluation indexes. RMSE and score of the prediction results are 12.35 and 284.1, respectively. It has higher prediction accuracy compared with traditional deep learning and machine learning methods.
Related Topics
Concepts
Particle swarm optimization
Computer science
Aero engine
Layer (electronics)
Hyperparameter
Mean squared error
Set (abstract data type)
Artificial neural network
Term (time)
Sliding window protocol
Artificial intelligence
Data mining
Machine learning
Window (computing)
Engineering
Mechanical engineering
Operating system
Statistics
Programming language
Quantum mechanics
Physics
Mathematics
Organic chemistry
Chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/00202940231214868
- OA Status
- gold
- Cited By
- 3
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390050912
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390050912Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1177/00202940231214868Digital Object Identifier
- Title
-
Engine remaining useful life prediction based on PSO optimized multi-layer long short-term memory and multi-source information fusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-21Full publication date if available
- Authors
-
Yuan Wei, Xinlong Li, Hongbin Gu, Faye Zhang, Fei MiaoList of authors in order
- Landing page
-
https://doi.org/10.1177/00202940231214868Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1177/00202940231214868Direct OA link when available
- Concepts
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Particle swarm optimization, Computer science, Aero engine, Layer (electronics), Hyperparameter, Mean squared error, Set (abstract data type), Artificial neural network, Term (time), Sliding window protocol, Artificial intelligence, Data mining, Machine learning, Window (computing), Engineering, Mechanical engineering, Operating system, Statistics, Programming language, Quantum mechanics, Physics, Mathematics, Organic chemistry, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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