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| Paper Break Fault Recognition in Long Process Papermaking Process Based on Autoencoder |
| Received:November 16, 2023 |
| DOI：10.11980/j.issn.0254-508X.2024.03.015 |
| Key Words:papermaking feature extraction stacked autoencoder fault recognition |
| Fund Project:广州市基础与应用基础研究（202201010356， 2023A04J1367）；制浆造纸国家重点实验室重点项目（2022ZD02）；中央高校基本科研业务费专项资金资助（2023ZYGXZR100）。 |
| Author Name | Affiliation | Postcode | | CHEN Guojian | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | LI Jigeng | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | CHEN Bo | Xi’an Aerospace Automation Co., Ltd., Xi’an, Shaanxi Province, 710065 | 710065 | | MAN Yi | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | HE Zhenglei* | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 |
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| Abstract:Paper breaks leading to unanticipated downtime can significantly impact the production efficiency， the prediction and diagnosis of paper break is rather important for the papermaking industry. In this study， a method of paper break fault classification and identification based on stacked autoencoder （SAE） and Softmax classifier was proposed to select the key process variables by combination of correlation analysis and mechanism analysis， and adopt clustering method to label the type of paper breaks. SAE model was established and trained to extract deep features from the data， and paper break faults were identified based on the extracted features using a Softmax classifier. Validated using historical data from a paper mill， and the model results were better than those of other machine learning models， with achieving the paper break classification accuracy of 96.2%， the precision of 93.1%， and the recall rate of 91.9%， which indicated that the classification model based on SAE feature extraction could effectively identify paper break faults. |
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