TMoE-P：多变量软测量的帕累托最优解
TMoE-P: Towards the Pareto Optimum for Multivariate Soft Sensors
摘要：
多变量软测量寻求使用可测量的过程变量来精确估计多个质量变量，这已经成为提高工业制造质量的关键因素。目前的进展停留在多任务网络架构的一些直接应用上;然而，这些方法仍有两个基本问题有待研究：（1）负转移，其中尽管不同目标的区别表示不同，但共享表示会降低性能;（2）跷跷板现象，其中优化器以牺牲其他目标为代价专注于一个主要但简单的目标。在这项研究中，我们将多变量软测量重新表述为多目标问题，以解决这两个问题并提高最先进的性能。为了处理负迁移问题，我们首先提出了一个基于目标感知的混合专家（OMoE）模块，利用目标特定和目标共享的专家进行参数共享，同时保持目标之间的区别。为了解决这种跷跷板现象，我们提出了一个Pareto目标路由（POR）模块，动态调整学习目标的权重，以达到Pareto最优，有坚实的理论支持。我们进一步提出了一个任务感知的混合专家框架，用于实现多变量软测量中的帕累托最优（TMoE-P），该框架由堆叠的OMoE模块和POR模块组成。我们用一个开放的软测量基准测试说明了TMoE-P的有效性，其中TMoE-P有效地消除了负迁移和跷跷板问题，并优于基线模型。
英文摘要：
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.
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