Multi-variate soft sensor seeks accurate estimation of multiple quality
variables using measurable process variables, which have emerged as a key
factor in improving the quality of industrial manufacturing. The current
progress stays in some direct applications of multitask network architectures;
however, there are two fundamental issues remain yet to be investigated with
these approaches: (1) negative transfer, where sharing representations despite
the difference of discriminate representations for different objectives
degrades performance; (2) seesaw phenomenon, where the optimizer focuses on one
dominant yet simple objective at the expense of others. In this study, we
reformulate the multi-variate soft sensor to a multi-objective problem, to
address both issues and advance state-of-the-art performance. To handle the
negative transfer issue, we first propose an Objective-aware Mixture-of-Experts
(OMoE) module, utilizing objective-specific and objective-shared experts for
parameter sharing while maintaining the distinction between objectives. To
address the seesaw phenomenon, we then propose a Pareto Objective Routing (POR)
module, adjusting the weights of learning objectives dynamically to achieve the
Pareto optimum, with solid theoretical supports. We further present a
Task-aware Mixture-of-Experts framework for achieving the Pareto optimum
(TMoE-P) in multi-variate soft sensor, which consists of a stacked OMoE module
and a POR module. We illustrate the efficacy of TMoE-P with an open soft sensor
benchmark, where TMoE-P effectively alleviates the negative transfer and seesaw
issues and outperforms the baseline models.

本文提出一种适用于多个质量变量的多变量软测量方法，通过改良神经网络结构和优化目标函数，实现了对质量变量的准确估计。在这个方法中，作者利用目标专家混合模块（OMoE）和帕累托目标路由模块（POR）解决了模型中存在的负面迁移和平衡问题，实验结果表明，该模型表现出了先进的性能。