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CHP Engine Anomaly Detection Based on Parallel CNN-LSTM with Residual Blocks and Attention
The extreme operating environment of the combined heat and power (CHP) engine is likely to cause anomalies and defects, which can lead to engine failure; thus, detecting engine anomalies is essential. In this study, we propose a parallel convolutional neural network–long short-term memory (CNN-LSTM)...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650369/ https://www.ncbi.nlm.nih.gov/pubmed/37960445 http://dx.doi.org/10.3390/s23218746 |