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A Data-driven Self-supervised LSTM-DeepFM Model for Industrial Soft Sensor
Soft sensor, as an important paradigm for industrial intelligence, is widely used in industrial pro- duction to achieve efficient monitoring and prediction of production status including product quality. Data-driven soft sensor methods have attracted attention, which still have challenges because of...
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Published in: | IEEE transactions on industrial informatics 2022-09, Vol.18 (9), p.1-1 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Soft sensor, as an important paradigm for industrial intelligence, is widely used in industrial pro- duction to achieve efficient monitoring and prediction of production status including product quality. Data-driven soft sensor methods have attracted attention, which still have challenges because of complex industrial data with diverse characteristics, nonlinear relationships mas- sive unlabeled samples. In this paper, a data-driven self- supervised long short term memory-deep factorization ma- chine model is proposed for industrial soft sensor, in which a framework mainly including pretraining and finetuning stages is proposed to explore diverse indus- trial data characteristics. In the pretraining stage, LSTM- Autoencoder is first unsupervised pretrained. Then, based on two self-supervised mask strategies, LSTM-Deep can explore the interdependencies between features the dynamic fluctuation in time series. In the finetuning stage, relying on pretrained representation, the temporal, high-dimensional and low-dimensional features can be ex- tracted from the LSTM, Deep and FM components respec- tively. |
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ISSN: | 1551-3203 1941-0050 |
DOI: | 10.1109/TII.2021.3131471 |