Time/Frequency Feature-Driven Ensemble Learning for Fault Detection

Abstract

This study addresses the problem of fault detection in industrial processes by developing a time/frequency feature-driven ensemble learning method. In contrast to the current works based on time domain ensemble learning, this approach adequately integrates the critical frequency domain information. The frequency domain information can be used to effectively enhance the fault detection performance in ensemble learning. Here, the feature ensemble net (FENet) is chosen to capture the time domain feature. The power spectral density (PSD)-based frequency domain feature extraction network can capture the frequency domain features. Bayesian inference can then be used to combine the fault detection results that rely on time/frequency domain features. The simulations of the Tennessee Eastman Process (TEP) demonstrate that the proposed method significantly outperforms traditional methods. The average fault detection rate (FDR) of TEP faults 3, 5, 9, 15, 16, and 21 is 90.63%, much higher than that of 75% by FENet with one feature transformation layer, and those of about 4% by principal component analysis (PCA) and dynamic PCA (DPCA). This research provides a promising framework for more advanced and reliable fault detection in industrial applications.