Abstract
The product quality has become increasingly important for the modern manufacturing processes. Due to the measurement delay, data-driven soft sensor models are usually built for the quality prediction in advance. While most prior works develop the customized model for a specific scenario, some recent works explore the adaptive mechanisms for the model to tolerate the online changes. However, they either tackle the operational variations due to changing product specifications for market demands, or deal with the latent variations due to process uncertainties such as sensor degradation. To improve the generalization towards diverse processes with both variations, a novel slow-fast dual-branch method inspired by the complementary learning systems in neuroscience is proposed for the first time. The slow branch is composed of an enhanced multi-layer perceptron with attention-based embedding fusion and memory aware synapses to grasp and consolidate the long-term global knowledge under non-independent and identically distributed data samples. The fast branch contains a modified broad learning system with maximum correntropy criterion and adaptive sample weights to rapidly track the short-term time-varying patterns. The two branches are integrated via feature sharing and refined gradient boosting to mimic the interactions between neocortex and hippocampus of brain. Extensive experiments on three real-world manufacturing processes from distinct industries show the superior performance of proposed method over 15 state-of-the-art methods.
Published Version
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