  Deep learning techniques have become prominent in modern fault diagnosis for
complex processes. In particular, convolutional neural networks (CNNs) have
shown an appealing capacity to deal with multivariate time-series data by
converting them into images. However, existing CNN techniques mainly focus on
capturing local or multi-scale features from input images. A deep CNN is often
required to indirectly extract global features, which are critical to describe
the images converted from multivariate dynamical data. This paper proposes a
novel local-global CNN (LG-CNN) architecture that directly accounts for both
local and global features for fault diagnosis. Specifically, the local features
are acquired by traditional local kernels whereas global features are extracted
by using 1D tall and fat kernels that span the entire height and width of the
image. Both local and global features are then merged for classification using
fully-connected layers. The proposed LG-CNN is validated on the benchmark
Tennessee Eastman process (TEP) dataset. Comparison with traditional CNN shows
that the proposed LG-CNN can greatly improve the fault diagnosis performance
without significantly increasing the model complexity. This is attributed to
the much wider local receptive field created by the LG-CNN than that by CNN.
The proposed LG-CNN architecture can be easily extended to other image
processing and computer vision tasks.
