  Early fault detection (EFD) of rotating machines is important to decrease the
maintenance cost and improve the mechanical system stability. One of the key
points of EFD is developing a generic model to extract robust and
discriminative features from different equipment for early fault detection.
Most existing EFD methods focus on learning fault representation by one type of
feature. However, a combination of multiple features can capture a more
comprehensive representation of system state. In this paper, we propose an EFD
method based on multiple feature fusion with stacking architecture (M2FSA). The
proposed method can extract generic and discriminiative features to detect
early faults by combining time domain (TD), frequency domain (FD), and
time-frequency domain (TFD) features. In order to unify the dimensions of the
different domain features, Stacked Denoising Autoencoder (SDAE) is utilized to
learn deep features in three domains. The architecture of the proposed M2FSA
consists of two layers. The first layer contains three base models, whose
corresponding inputs are different deep features. The outputs of the first
layer are concatenated to generate the input to the second layer, which
consists of a meta model. The proposed method is tested on three bearing
datasets. The results demonstrate that the proposed method is better than
existing methods both in sensibility and reliability.
