Instrumentation & Measurement Magazine 26-2 - 42
Fig. 1. Corner points in the RGB and the grayscale images. (a) RGB image; (b)
Grayscale image.
of corresponding two points in the time series. On the other
hand, the RGB image provides more corner points compared
to the grayscale image under the same length of the signal segment,
i.e., the RGB image contains more significant features
for feature extraction and fault classification. Fig. 1 demonstrates
of the RGB image and the grayscale image generated
by the interception and rearrangement method, where the
additional Harris corner information is highlighted in blue.
The original vibration signal segment of length L is converted
into a representation of an three channeled n×n time series by
STI conversion. However, the traditional truncation and rearrangement
method generates a two-dimensional image with
the size of 4×n. The derived RGB image gets more pixels than
the traditionally converted image on the same length of signal
segment. Moreover, from Fig. 1, we can see that compared
to the grayscale image, there are more highlighted corners in
the RGB image, i.e., the RGB image contains more significant
characteristics for the feature extraction and following fault
classification in the network diagnosis model.
Dense ResNet Model
The ResNet network model is composed of stacked residual
block structures to deal with the gradient back propagation
and weight updating. To improve the model adaption to
variable working conditions and complex background environments
with noise, a dense connection ResNet is constructed
to realize the best use of limited feature information with
a small number of labels in the target domain. The derived
DResNet network is shown in Fig. 2. It takes on four serially
connected ResNet layers while strengthening the global feature
extraction by integrating the dense connections across
every layer.
ResNet layer realizes the fusion of the shallow feature and
deep feature. The pre-activation is employed between the
ResNet layers for the standardization of the expanded feature
map. Its output feature map is governed by:
GX 0
where Xi
42
iiconv[Relu(BN( ))]
(3)
is the input feature map of the layer i, i =1, 2, 3, 4; BN(.)
is the batch normalization; Relu(.) is the activation function;
IEEE Instrumentation & Measurement Magazine
Fig. 2. The structure of the DResNet.
and conv[.] means a convolutional layer with m convolution
kernels sized of 1×1. It is described as:
x m nn
n x kbr rr
m
r
where x , k and n
n
r
n
r
r
1
corresponding bias of the rth layer, respectively; and x 1 is the
m
r
mth output of the r-1th layer.
Specifically for the layer 1, it gives:
G conv( )X
01
(5)
April 2023
(4)
b are the nth output, convolution kernel and
Instrumentation & Measurement Magazine 26-2
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