ArXiv Preprint
Fault detection and diagnosis is significant for reducing maintenance costs
and improving health and safety in chemical processes. Convolution neural
network (CNN) is a popular deep learning algorithm with many successful
applications in chemical fault detection and diagnosis tasks. However,
convolution layers in CNN are very sensitive to the order of features, which
can lead to instability in the processing of tabular data. Optimal order of
features result in better performance of CNN models but it is expensive to seek
such optimal order. In addition, because of the encapsulation mechanism of
feature extraction, most CNN models are opaque and have poor interpretability,
thus failing to identify root-cause features without human supervision. These
difficulties inevitably limit the performance and credibility of CNN methods.
In this paper, we propose an order-invariant and interpretable hierarchical
dilated convolution neural network (HDLCNN), which is composed by feature
clustering, dilated convolution and the shapley additive explanations (SHAP)
method. The novelty of HDLCNN lies in its capability of processing tabular data
with features of arbitrary order without seeking the optimal order, due to the
ability to agglomerate correlated features of feature clustering and the large
receptive field of dilated convolution. Then, the proposed method provides
interpretability by including the SHAP values to quantify feature contribution.
Therefore, the root-cause features can be identified as the features with the
highest contribution. Computational experiments are conducted on the Tennessee
Eastman chemical process benchmark dataset. Compared with the other methods,
the proposed HDLCNN-SHAP method achieves better performance on processing
tabular data with features of arbitrary order, detecting faults, and
identifying the root-cause features.
Mengxuan Li, Peng Peng, Min Wang, Hongwei Wang
2023-02-13
