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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Springer
2025-05-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07011-3 |
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| author | Yao Zhang Pengfei Zhang Wenchao Zhang Mingwei Wang |
| author_facet | Yao Zhang Pengfei Zhang Wenchao Zhang Mingwei Wang |
| author_sort | Yao Zhang |
| collection | DOAJ |
| description | 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), long short-term memory network (LSTM), attention mechanism (Attention), and particle swarm optimization (PSO). The Attention mechanism is used to assign weights to input features, enhancing the model focus on important data. The CNN and LSTM networks are used to extract features and capture long-term dependencies in time series, respectively. Additionally, the PSO algorithm optimized the number of neurons and the learning rate in the model. For early fault detection, the Mahalanobis distance-based function mapping method is established, along with the PMT monitoring index, early warning, and alarm threshold. Experimental results show that the mean absolute error (MAE) is reduced by 0.5656 and the coefficient of determination (R 2 ) is increased by 0.0619 in the CNN-LSTM-AP compared with other 5 prediction models. In fault scenarios, the PMT index drops below the warning threshold five consecutive timepoints before failure, enabling proactive alerts. The deep learning model and warning method proposed in this paper can accurately predict the condition of the meat grinder and issue a warning of potential faults based on the PMT data, offering a novel approach to the health management of meat processing equipment. |
| format | Article |
| id | doaj-art-4e1c2127ebfa4e35bb0a21bc075c6926 |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-4e1c2127ebfa4e35bb0a21bc075c69262025-08-20T02:10:50ZengSpringerDiscover Applied Sciences3004-92612025-05-017511810.1007/s42452-025-07011-3CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motorsYao Zhang0Pengfei Zhang1Wenchao Zhang2Mingwei Wang3School of Mechanical Engineering and Automation, SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic UniversitySchool of Mechanical Engineering and Automation, SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic UniversitySchool of Mechanical Engineering and Automation, SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic UniversitySchool of Mechanical Engineering and Automation, SKL of Marine Food Processing & Safety Control, National Engineering Research Center of Seafood, Dalian Polytechnic UniversityAbstract 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), long short-term memory network (LSTM), attention mechanism (Attention), and particle swarm optimization (PSO). The Attention mechanism is used to assign weights to input features, enhancing the model focus on important data. The CNN and LSTM networks are used to extract features and capture long-term dependencies in time series, respectively. Additionally, the PSO algorithm optimized the number of neurons and the learning rate in the model. For early fault detection, the Mahalanobis distance-based function mapping method is established, along with the PMT monitoring index, early warning, and alarm threshold. Experimental results show that the mean absolute error (MAE) is reduced by 0.5656 and the coefficient of determination (R 2 ) is increased by 0.0619 in the CNN-LSTM-AP compared with other 5 prediction models. In fault scenarios, the PMT index drops below the warning threshold five consecutive timepoints before failure, enabling proactive alerts. The deep learning model and warning method proposed in this paper can accurately predict the condition of the meat grinder and issue a warning of potential faults based on the PMT data, offering a novel approach to the health management of meat processing equipment.https://doi.org/10.1007/s42452-025-07011-3Meat grinderConvolutional neural networkLong short-term memory neural networkTemperature predictionFault warning |
| spellingShingle | Yao Zhang Pengfei Zhang Wenchao Zhang Mingwei Wang CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors Discover Applied Sciences Meat grinder Convolutional neural network Long short-term memory neural network Temperature prediction Fault warning |
| title | CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors |
| title_full | CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors |
| title_fullStr | CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors |
| title_full_unstemmed | CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors |
| title_short | CNN-LSTM-Attention with PSO optimization for temperature and fault prediction in meat grinder motors |
| title_sort | cnn lstm attention with pso optimization for temperature and fault prediction in meat grinder motors |
| topic | Meat grinder Convolutional neural network Long short-term memory neural network Temperature prediction Fault warning |
| url | https://doi.org/10.1007/s42452-025-07011-3 |
| work_keys_str_mv | AT yaozhang cnnlstmattentionwithpsooptimizationfortemperatureandfaultpredictioninmeatgrindermotors AT pengfeizhang cnnlstmattentionwithpsooptimizationfortemperatureandfaultpredictioninmeatgrindermotors AT wenchaozhang cnnlstmattentionwithpsooptimizationfortemperatureandfaultpredictioninmeatgrindermotors AT mingweiwang cnnlstmattentionwithpsooptimizationfortemperatureandfaultpredictioninmeatgrindermotors |