When just-in-time learning meets deep learning: An industrial quality prediction practice on deep partial least squares model

Junhua Zheng, Zeyu Yang, Zhiqiang Ge,
When just-in-time learning meets deep learning: An industrial quality prediction practice on deep partial least squares model,
Chemometrics and Intelligent Laboratory Systems,
Volume 267,
2025,
105555,
ISSN 0169-7439,
https://doi.org/10.1016/j.chemolab.2025.105555.
(https://www.sciencedirect.com/science/article/pii/S0169743925002400)
Abstract: While deep learning has made great progresses in various application domains, the nature of computational expensive and reliance on large-scale data makes it inefficient or even impossible for small data modeling, particularly under the just-in-time learning framework. Effective combination of deep learning and just-in-time learning may explore great potentials for both two learning paradigms, thus should be attractive and beneficial to the research community. In this paper, an improved form of the lightweight deep partial least squares (PLS) model is developed under the framework of Just-in-time learning. Without complicated backpropagation and time-consuming parameter tuning algorithms, deep PLS provides a transparent model structure which also works well for small training data. As a result, fusion of those two learning strategies makes the new proposed method as a very promising predictive modeling tool in industrial soft sensor applications, the performance of which is evaluated and confirmed through a real industrial example.
Keywords: Just-in-time learning; Lightweight deep learning; Partial least squares; Quality prediction; Local predictive modeling