In the manufacturing process, sensor data collected from equipment is crucial for building predictive models to manage processes and improve productivity. However, in the field, it is challenging to gather sufficient data to build robust models. This study proposes a novel predictive model based on the Transformer, utilizing statistical feature embedding and window positional encoding. Statistical features provide an effective representation of sensor data, and the embedding enables the Transformer to learn both time- and sensor-related information. Window positional encoding captures precise time details from the feature embedding. The model's performance is evaluated in two problems: fault detection and virtual metrology, showing superior results compared to baseline models. This improvement is attributed to the efficient use of parameters, which is particularly beneficial for sensor data that often has limited sample sizes. The results support the model's applicability across various manufacturing industries, demonstrating its potential for enhancing process management and yield.

翻译：在制造过程中，从设备采集的传感器数据对于构建预测模型以管理流程和提升生产效率至关重要。然而，在实际工业场景中，获取足够数据以构建稳健模型具有挑战性。本研究提出一种基于Transformer的新型预测模型，采用统计特征嵌入与窗口位置编码技术。统计特征为传感器数据提供了有效的表征方式，而特征嵌入使Transformer能够同时学习时间相关与传感器相关的信息。窗口位置编码则从特征嵌入中捕获精确的时间细节。该模型在故障检测与虚拟量测两个问题上进行了性能评估，结果显示其优于基准模型。这一改进归因于模型对参数的高效利用，这对于样本量通常有限的传感器数据尤为有益。研究结果支持该模型在各类制造行业中的适用性，展现了其在优化流程管理与提升良率方面的潜力。