Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE
To address potential unnecessary shutdowns caused by quality-unrelated faults during batch fermentation processes, the paper proposed a noise semi-supervised stacked auto-encoder (NSSAE) to differentiate the quality-relevant and the quality-irrelevant faults. First, mutual information was applied to...
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| Format: | Article |
| Language: | zho |
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The editorial department of Science and Technology of Food Industry
2025-02-01
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| Series: | Shipin gongye ke-ji |
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| Online Access: | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024020300 |
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| author | Zhong LIU Zheng ZHANG Xuyang LOU Jinlin ZHU |
| author_facet | Zhong LIU Zheng ZHANG Xuyang LOU Jinlin ZHU |
| author_sort | Zhong LIU |
| collection | DOAJ |
| description | To address potential unnecessary shutdowns caused by quality-unrelated faults during batch fermentation processes, the paper proposed a noise semi-supervised stacked auto-encoder (NSSAE) to differentiate the quality-relevant and the quality-irrelevant faults. First, mutual information was applied to calculate the contribution from the process variables to quality variables, where artificial noised was introduced to enhance the performance. Second, an NSSAE-based monitoring model was established, wherein indicators for faults and quality variations were separately constructed from the first layer and the last layer of the model. Upon which, kernel density estimation was used to calculate the thresholds for the indicators. Lastly, deep reconstruction-based contribution was used to locate the root cause. Based on the results of numerical simulations and lactic acid bacteria batch fermentation experiments, the NSSAE algorithm proposed in this paper demonstrated the ability to accurately distinguish between quality-related and quality-irrelevant faults. The fault detection rate using the detection index of the first layer of residual space approached 100%. Moreover, the detection index in the final layer of latent space could precisely identify both quality-related and quality-irrelevant faults. Utilizing the DRBC diagnostic method, the specific variable causing the fault can be accurately pinpointed post-fault occurrence. These findings suggest a practical and effective process monitoring method for addressing quality-related and quality-irrelevant fault monitoring issues in the batch fermentation process. |
| format | Article |
| id | doaj-art-806a7912dc164f8bb2623d3e5dd93c54 |
| institution | Kabale University |
| issn | 1002-0306 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | The editorial department of Science and Technology of Food Industry |
| record_format | Article |
| series | Shipin gongye ke-ji |
| spelling | doaj-art-806a7912dc164f8bb2623d3e5dd93c542025-01-21T07:24:08ZzhoThe editorial department of Science and Technology of Food IndustryShipin gongye ke-ji1002-03062025-02-0146311010.13386/j.issn1002-0306.20240203002024020300-3Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAEZhong LIU0Zheng ZHANG1Xuyang LOU2Jinlin ZHU3School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaDepartment of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaSchool of Food Science and Technology, Jiangnan University, Wuxi 214122, ChinaTo address potential unnecessary shutdowns caused by quality-unrelated faults during batch fermentation processes, the paper proposed a noise semi-supervised stacked auto-encoder (NSSAE) to differentiate the quality-relevant and the quality-irrelevant faults. First, mutual information was applied to calculate the contribution from the process variables to quality variables, where artificial noised was introduced to enhance the performance. Second, an NSSAE-based monitoring model was established, wherein indicators for faults and quality variations were separately constructed from the first layer and the last layer of the model. Upon which, kernel density estimation was used to calculate the thresholds for the indicators. Lastly, deep reconstruction-based contribution was used to locate the root cause. Based on the results of numerical simulations and lactic acid bacteria batch fermentation experiments, the NSSAE algorithm proposed in this paper demonstrated the ability to accurately distinguish between quality-related and quality-irrelevant faults. The fault detection rate using the detection index of the first layer of residual space approached 100%. Moreover, the detection index in the final layer of latent space could precisely identify both quality-related and quality-irrelevant faults. Utilizing the DRBC diagnostic method, the specific variable causing the fault can be accurately pinpointed post-fault occurrence. These findings suggest a practical and effective process monitoring method for addressing quality-related and quality-irrelevant fault monitoring issues in the batch fermentation process.http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024020300batch fermentation processquality-related faultsnoised semi-supervised stacked auto-encoderfault detection and diagnosisdeep reconstruction-based contribution |
| spellingShingle | Zhong LIU Zheng ZHANG Xuyang LOU Jinlin ZHU Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE Shipin gongye ke-ji batch fermentation process quality-related faults noised semi-supervised stacked auto-encoder fault detection and diagnosis deep reconstruction-based contribution |
| title | Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE |
| title_full | Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE |
| title_fullStr | Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE |
| title_full_unstemmed | Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE |
| title_short | Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE |
| title_sort | quality related and quality irrelevant fault detection and diagnosis in batch fermentation process based on nssae |
| topic | batch fermentation process quality-related faults noised semi-supervised stacked auto-encoder fault detection and diagnosis deep reconstruction-based contribution |
| url | http://www.spgykj.com/cn/article/doi/10.13386/j.issn1002-0306.2024020300 |
| work_keys_str_mv | AT zhongliu qualityrelatedandqualityirrelevantfaultdetectionanddiagnosisinbatchfermentationprocessbasedonnssae AT zhengzhang qualityrelatedandqualityirrelevantfaultdetectionanddiagnosisinbatchfermentationprocessbasedonnssae AT xuyanglou qualityrelatedandqualityirrelevantfaultdetectionanddiagnosisinbatchfermentationprocessbasedonnssae AT jinlinzhu qualityrelatedandqualityirrelevantfaultdetectionanddiagnosisinbatchfermentationprocessbasedonnssae |