An Explainable Autoencoder-Based Feature Extraction Combined with CNN-LSTM-PSO Model for Improved Predictive Maintenance
Source abstract
Abstract
Predictive maintenance plays a crucial role in preventing equipment failures and minimizing operational downtime in modern industries. However, traditional predictive maintenance methods often face challenges in adapting to d... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Big Data Technologies and Applications · Traffic Prediction and Management Techniques · Advanced Decision-Making Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (27)
Show source-provided references
1. Integrated model-based control allocation strategies oriented to predictive maintenance of saturated actuators10.1109/TASE.2024.3358912 ↗
2. Reliability modeling in a predictive maintenance context: a margin-based approach10.1016/j.ress.2023.109861 ↗
3. Yazdi · Advances in computational mathematics for industrial system reliability and maintainability · 2024 · 4310.1007/978-3-031-53514-7_3 ↗
4. A critical insight and evaluation of AI models for predictive maintenance under Industry 4.0
5. Prognostics and health management for predictive maintenance: a review10.1016/j.jmsy.2024.05.021 ↗
6. Machine learning and IoT-based predictive maintenance approach for industrial applications10.1016/j.aej.2023.12.065 ↗
7. Machine learning Scopes on microgrid predictive maintenance: potential frameworks, challenges, and prospects10.1016/j.rser.2023.114088 ↗
8. MachNet, a general deep learning architecture for predictive maintenance within the Industry 4.0 paradigm10.1016/j.engappai.2023.107365 ↗
9. Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance10.1016/j.datak.2023.102240 ↗
10. Dynamic predictive maintenance strategy for system remaining useful life prediction via deep learning ensemble method10.1016/j.ress.2024.110012 ↗
11. A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning10.1016/j.ress.2023.109707 ↗
12. A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance10.1016/j.ress.2023.109723 ↗
13. Predicting maintenance through an attention long short-term memory projected model10.1007/s10845-023-02077-5 ↗
14. FUCOM-optimization based predictive maintenance strategy using expert elicitation and Artificial Neural Network10.1016/j.eswa.2023.121322 ↗
15. Big data, machine learning, and digital twin assisted additive manufacturing: a review10.1016/j.matdes.2024.113086 ↗
16. Prediction maintenance based on vibration analysis and deep learning—a case study of a drying press supported on a Hidden Markov Model10.1016/j.asoc.2024.111885 ↗
17. A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant10.1007/s10845-023-02281-3 ↗
18. Data-driven drift detection and diagnosis framework for predictive maintenance of heterogeneous production processes: application to a multiple tapping process10.1016/j.engappai.2024.109552 ↗
19. IoV-twinChain: predictive maintenance of vehicles in internet of vehicles through digital twin and blockchain10.1016/j.iot.2025.101514 ↗
20. Explainable artificial intelligence of tree-based algorithms for fault detection and diagnosis in grid-connected photovoltaic systems10.1016/j.engappai.2024.109503 ↗
21. Explainable artificial intelligence for predictive maintenance applications
22. The application of machine learning in real-time monitoring for US manufacturing and logistics
23. Predictive maintenance for offshore wind turbines through deep learning and online clustering of unsupervised subsystems: a real-world implementation10.1007/s40722-024-00335-z ↗
24. Explainable artificial intelligence model for predictive maintenance in smart agricultural facilities10.1109/ACCESS.2024.3365586 ↗
25. LSTM-autoencoder-based interpretable predictive maintenance framework for industrial systems
26. Predictive maintenance optimization in zigbee-enabled smart home networks: a machine learning-driven approach utilizing fault prediction models
27. Early fault prediction for wind turbines based on deep learning