  Compared with traditional model-based fault detection and classification
(FDC) methods, deep neural networks (DNN) prove to be effective for the
aerospace sensors FDC problems. However, time being consumed in training the
DNN is excessive, and explainability analysis for the FDC neural network is
still underwhelming. A concept known as imagefication-based intelligent FDC has
been studied in recent years. This concept advocates to stack the sensors
measurement data into an image format, the sensors FDC issue is then
transformed to abnormal regions detection problem on the stacked image, which
may well borrow the recent advances in the machine vision vision realm.
Although promising results have been claimed in the imagefication-based
intelligent FDC researches, due to the low size of the stacked image, small
convolutional kernels and shallow DNN layers were used, which hinders the FDC
performance. In this paper, we first propose a data augmentation method which
inflates the stacked image to a larger size (correspondent to the VGG16 net
developed in the machine vision realm). The FDC neural network is then trained
via fine-tuning the VGG16 directly. To truncate and compress the FDC net size
(hence its running time), we perform model pruning on the fine-tuned net. Class
activation mapping (CAM) method is also adopted for explainability analysis of
the FDC net to verify its internal operations. Via data augmentation,
fine-tuning from VGG16, and model pruning, the FDC net developed in this paper
claims an FDC accuracy 98.90% across 4 aircraft at 5 flight conditions (running
time 26 ms). The CAM results also verify the FDC net w.r.t. its internal
operations.
