CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors

CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors

Abstract Meat grinders are essential in meat production, and predicting the working parameters of the meat grinder motors can help prevent potential failures by providing timely warnings. In this paper, a deep learning model, CNN-LSTM-AP, is developed, combining convolutional neural network (CNN), l...

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Bibliographic Details
Main Authors: Yao Zhang, Pengfei Zhang, Wenchao Zhang, Mingwei Wang
Format: Article
Language:English
Published: Springer 2025-05-01
Series:Discover Applied Sciences
Subjects:
Meat grinder
Convolutional neural network
Long short-term memory neural network
Temperature prediction
Fault warning
Online Access:https://doi.org/10.1007/s42452-025-07011-3
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https://doi.org/10.1007/s42452-025-07011-3

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