A Semi-Supervised Quality Soft Sensing Method Under Multiple Operating Conditions for Complex Product Manufacturing Processes

The accurate perception of key quality variables in complex manufacturing processes is a necessary prerequisite for achieving system optimization control and ensuring the safe and stable operation of the system. Consider that the manufacturing process has complex characteristics, such as multioperating conditions, time-varying behavior, nonlinearity, and scarcity of labeled samples, making accurate perception of process quality more difficult. This article proposes a semi-supervised quality soft sensing method under multiple operating conditions for complex product manufacturing processes. First, a dual k-means operating condition identification method assisted by deep metric learning (DML) is proposed, aiming to achieve a fine-grained partition of online data into specific operating conditions and the recognition of new operating conditions. Second, an online selective perception strategy is employed, wherein different approaches are taken based on whether the samples belong to existing operating conditions. In the construction of the base model, both spatial and temporal information among the samples are simultaneously considered, and the results are fused through an adaptive fusion strategy. Furthermore, considering the scarcity of labeled samples in real industrial processes, a high-confidence pseudolabel sample construction method based on just-in-time learning (JITL) and genetic algorithms (GAs) is proposed. This approach aims to obtain high-confidence pseudolabel samples to augment the modeling dataset. Finally, the effectiveness of the proposed method is validated through a real manufacturing process, the float glass production process.