  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.
