Autoencoder-assisted Feature Ensemble Net for Incipient Faults

TL;DR

This paper introduces AE-FENet, a deep feature ensemble framework using autoencoders to improve detection of difficult incipient faults in industrial processes, achieving over 96% accuracy.

Contribution

The paper presents a novel autoencoder-assisted feature ensemble network that enhances fault detection performance over traditional PCA-based methods for incipient faults.

Findings

Achieves over 96% accuracy on difficult faults in TEP.

Autoencoder-based features outperform PCA in fault detection.

Framework extends to various deep learning detectors.

Abstract

Deep learning has shown the great power in the field of fault detection. However, for incipient faults with tiny amplitude, the detection performance of the current deep learning networks (DLNs) is not satisfactory. Even if prior information about the faults is utilized, DLNs can't successfully detect faults 3, 9 and 15 in Tennessee Eastman process (TEP). These faults are notoriously difficult to detect, lacking effective detection technologies in the field of fault detection. In this work, we propose Autoencoder-assisted Feature Ensemble Net (AE-FENet): a deep feature ensemble framework that uses the unsupervised autoencoder to conduct the feature transformation. Compared with the principle component analysis (PCA) technique adopted in the original Feature Ensemble Net (FENet), autoencoder can mine more exact features on incipient faults, which results in the better detection performance of AE-FENet. With same kinds of basic detectors, AE-FENet achieves a state-of-the-art average accuracy over 96% on faults 3, 9 and 15 in TEP, which represents a significant enhancement in performance compared to other methods. Plenty of experiments have been done to extend our framework, proving that DLNs can be utilized efficiently within this architecture.