Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data
Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness of nonlinear feature extraction and strong expansibility, an autoencoder (AE) and its extensions have been widely developed for industrial a...
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| Vydané v: | IEEE transactions on industrial informatics Ročník 17; číslo 1; s. 260 - 269 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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IEEE
01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1551-3203, 1941-0050 |
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| Abstract | Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness of nonlinear feature extraction and strong expansibility, an autoencoder (AE) and its extensions have been widely developed for industrial applications. Nevertheless, an AE commonly uses the last hidden layer for regression modeling with the output, which seems to be a kind of information waste as the shallow layers are also abstractions of input data. Besides, when there are excessive unlabeled samples, AE-based models are less likely to make full use of them or even degrade the performance. To deal with these issues, a method called ensemble semi-supervised gated stacked AE (ES 2 GSAE) is proposed in this article. Gate units are used to develop connections between different layers and the output layer, which also help quantify the contribution of different hidden layers. Moreover, the idea of ensemble learning is combined with semi-supervised learning, in which different unlabeled datasets are used for training different submodels to ensure their diversities. In this way, unlabeled samples can be utilized more efficiently and help enhance the model performance. The effectiveness and superiority are verified in a real industrial process by comparing the proposed method with other typical AE-based models. |
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| AbstractList | Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness of nonlinear feature extraction and strong expansibility, an autoencoder (AE) and its extensions have been widely developed for industrial applications. Nevertheless, an AE commonly uses the last hidden layer for regression modeling with the output, which seems to be a kind of information waste as the shallow layers are also abstractions of input data. Besides, when there are excessive unlabeled samples, AE-based models are less likely to make full use of them or even degrade the performance. To deal with these issues, a method called ensemble semi-supervised gated stacked AE (ES 2 GSAE) is proposed in this article. Gate units are used to develop connections between different layers and the output layer, which also help quantify the contribution of different hidden layers. Moreover, the idea of ensemble learning is combined with semi-supervised learning, in which different unlabeled datasets are used for training different submodels to ensure their diversities. In this way, unlabeled samples can be utilized more efficiently and help enhance the model performance. The effectiveness and superiority are verified in a real industrial process by comparing the proposed method with other typical AE-based models. Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness of nonlinear feature extraction and strong expansibility, an autoencoder (AE) and its extensions have been widely developed for industrial applications. Nevertheless, an AE commonly uses the last hidden layer for regression modeling with the output, which seems to be a kind of information waste as the shallow layers are also abstractions of input data. Besides, when there are excessive unlabeled samples, AE-based models are less likely to make full use of them or even degrade the performance. To deal with these issues, a method called ensemble semi-supervised gated stacked AE (ES2GSAE) is proposed in this article. Gate units are used to develop connections between different layers and the output layer, which also help quantify the contribution of different hidden layers. Moreover, the idea of ensemble learning is combined with semi-supervised learning, in which different unlabeled datasets are used for training different submodels to ensure their diversities. In this way, unlabeled samples can be utilized more efficiently and help enhance the model performance. The effectiveness and superiority are verified in a real industrial process by comparing the proposed method with other typical AE-based models. |
| Author | Sun, Qingqiang Ge, Zhiqiang |
| Author_xml | – sequence: 1 givenname: Qingqiang orcidid: 0000-0002-7042-5640 surname: Sun fullname: Sun, Qingqiang email: sunqingqiang@zju.edu.cn organization: Department of Control Science and Engineering, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Zhiqiang orcidid: 0000-0002-2071-4380 surname: Ge fullname: Ge, Zhiqiang email: gezhiqiang@zju.edu.cn organization: Department of Control Science and Engineering, Zhejiang University, Hangzhou, China |
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| SubjectTerms | Data models Deep learning Deep learning (DL) Ensemble learning Feature extraction gated neurons Industrial applications Logic gates Neurons Performance degradation semi-supervised learning Sensors soft sensor stacked autoencoder (SAE) Training |
| Title | Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data |
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