Drift‐Aware Feature Learning Based on Autoencoder Preprocessing for Soft Sensors

Abstract

In this article, a novel approach is presented for drift‐aware feature learning aimed at calibrating drift biases in soft sensors for long‐term use. The proposed method leverages an autoencoder for data preprocessing to extract expressive signal drift traces features, and incorporates drift characteristics through the latent space representation in a long short‐term memory (LSTM) regression neural network. In the results, it is demonstrated that the proposed approach outperforms other typical recurrent neural networks, such as LSTM, gated recurrent unit, and bidirectional LSTM, with a reduced root mean square error of 60% for the training dataset (≈2.5 h) and 80% for the testing dataset (≈20 h). The proposed approach has the potential to optimize the performance of soft sensors with long‐term drift and reduce the need for frequent recalibration. By compensating for sensor drift using existing prior information and limited time data, the proposed neural network can effectively reduce the complexity and computational burden of the system, without the need for additional settings or hyperparameter fine‐tuning.