Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

Recently, stacked autoencoder (SAE)-based deep learning has been widely employed for industrial process fault detection, which can extract representative low-dimensional features. However, SAE fails to take the distribution structure of raw data into consideration in the unsupervised self-reconstruction learning, and this may lead to limited monitoring performance. Focusing on this issue, this paper proposes a new fault detection method based on entropy estimation density peak clustering and stacked autoencoder (EEDPC-SAE) for industrial process monitoring. First, an improved automatic clustering algorithm, namely, entropy estimation density peak clustering (EEDPC), is presented to analyze the distribution structure of process data. Then, the data distribution information is integrated into the learning procedure of SAE to capture the data intrinsic structure. EEDPC-SAE can describe the distribution characteristics of process data, and more informative high-level features can be learned for fault detection. Finally, two statistics, that is, Hotelling's T-squared (T-2) and squared prediction error (SPE), are established based on the encoder features and residual features generated by EEDPC-SAE. The effectiveness of the proposed method is evaluated by the Tennessee Eastman (TE) process and fed-batch fermentation penicillin (FBFP) process.