Data-driven soft sensor development based on deep learning technique

By Chao Shang, Fan Yang, Dexian Huang, Wenxiang Lyu. 2014

521 citations recorded by the source corpus.

How researchers see this paper

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Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018, unknown): “relationships [17]–[19].”
Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE (2018, background): “They discussed the unique advantages of deep learning for industrial processes [25].”
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “As a result, DBNs have been applied in a wide spectrum of areas, including fault diagnosis [194], soft sensor [196], and drug discovery [199].”
Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network (2020, background): “Deep learning has also been introduced and exploited for inferential modeling of key quality variables in industrial processes [14]–[16].”
A Survey on Deep Learning for Data-Driven Soft Sensors (2021, methodology): “…of most of the work reviewed in this survey is veriﬁed by doing numerical simulation experiments (e.g., [93], [114], etc.), or by using public available benchmark datasets (e.g., [137]), or by modeling the datasets from real-world processes (e.g., [91], [92], [93], [108], [114], [121], etc.).”