关键词: 工艺过程质量, 时序关联预测, 序列到序列, 时间卷积神经网络, 迁移学习

Abstract:  To address the problems which were difficult to accurately predict production quality due to the temporal coupling of multiple processing parameters in process production, a fast and efficient production quality prediction method was proposed based on deep TCN networks and migration learning. With a sequence-to-sequence learning structure, a deep TCN and a temporal attention mechanism formed the encoding component for extracting key temporal features from multiple sources, while a residual long short term memory network formed the decoding component for simultaneous extraction of quality temporal information, and migration learning was introduced to address the adaptability of the prediction model to online production quality prediction. The experiments show that the proposed method has significant advantages in prediction accuracy and stability, and has high prediction accuracy and computational efficiency in predicting small sample data.

Key words: process quality, time series correlation prediction, sequence to sequence, temporal convolution network(TCN), transfer learning