LAUSR

Soft sensor, as an important paradigm for industrial intelligence, is widely used in industrial production to achieve efficient monitoring and prediction of production status including product quality. Data-driven soft sensor methods have attracted attention, which still have challenges because of complex industrial data with diverse characteristics, nonlinear relationships, and massive unlabeled samples. In this article, a data-driven self-supervised long short-term memory–deep factorization machine (LSTM-DeepFM) model is proposed for industrial soft sensor, in which a framework mainly including pretraining and finetuning stages is proposed to explore diverse industrial data characteristics. In the pretraining stage, an LSTM-autoencoder is first unsupervised pretrained. Then, based on two self-supervised mask strategies, LSTM-deep can explore the interdependencies between features as well as the dynamic fluctuation in time series. In the finetuning stage, relying on pretrained representation, the temporal, high-dimensional, and low-dimensional features can be extracted from the LSTM, deep, and FM components, respectively. Finally, experiments on the real-world mining dataset demonstrate that the proposed method achieves state of the art comparing with stacked autoencoder-based models, variational autoencoder-based models, semisupervised parallel DeepFM, etc.