Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data
Qingqiang Sun, Zhiqiang Ge
Abstract
Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness of nonlinear feature extraction and strong expansibility, an autoencoder (AE) and its extensions have been widely developed for industrial applications. Nevertheless, an AE commonly uses the last hidden layer for regression modeling with the output, which seems to be a kind of information waste as the shallow layers are also abstractions of input data. Besides, when there are excessive unlabeled samples, AE-based models are less likely to make full use of them or even degrade the performance. To deal with these issues, a method called ensemble semi-supervised gated stacked AE (ES <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GSAE) is proposed in this article. Gate units are used to develop connections between different layers and the output layer, which also help quantify the contribution of different hidden layers. Moreover, the idea of ensemble learning is combined with semi-supervised learning, in which different unlabeled datasets are used for training different submodels to ensure their diversities. In this way, unlabeled samples can be utilized more efficiently and help enhance the model performance. The effectiveness and superiority are verified in a real industrial process by comparing the proposed method with other typical AE-based models.