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Page 140 Liu et al. Intell Robot 2023;3(2):131-43 I http://dx.doi.org/10.20517/ir.2023.07

Table 4. The accuracy of different domain adaptation methods in SEU datasets (%)

Method No-TL AdaBN MKMMD CORAL JMMD DA CDA OURS
Task
45.43 49.21 59.97 50.59 65.40 54.40 59.53 75.95
0-1(TD)
Task
1-0(TD) 56.16 57.89 67.45 58.44 68.62 58.80 65.54 72.87
Task
0-1(FD) 35.19 41.38 44.57 42.52 45.45 43.70 43.26 50.15
Task
42.99 49.53 44.28 51.17 61.29 53.96 52.93 62.90
1-0(FD)

are 1000 samples for each data type, and each sample is 1024 in length. Thus, this dataset consists of 9,000
data samples. Finally, we use 80% of the data to obtain the diagnostic model and 20% of the data to verify its
effectiveness.

In this experiment, to demonstrate the transfer effectiveness of the proposed method under different load and
velocity operating conditions, we collected vibration signals for two different states, 20Hz-0V and 30Hz-2V,
and named Task 0 and Task 1, respectively. We validated the model by combining the vibration signals for
the two states in a two-by-two fashion. In addition, two additional different signal forms were set up, with
both time and frequency domain signals considered as inputs, and a total of four different transfer tasks were
designed to validate the model. In order to evaluate the performance of our method in this case of widely
varying data distributions with different load and speed conditions, comparative tests were carried out with
some commonly used domain adaptation algorithms, such as MKMMD, CORAL, JMMD, DA, and CDA. In
this case, we also choose average accuracy as a key assessment metric.

4.3.2. Experimental results and analysis

The comparative results of the eight different methods on the bearing dataset are shown in Table 4. The results
of the four transfer tasks under different load and speed conditions show that our method still performs the
best of the eight different methods on this dataset, with the best average accuracy in all four transfer tasks.
It must be noted that a high level of accuracy is not achieved on this dataset, and it is evident that there are
significant differences in the data distribution between the two domains on this task. The main reason lies
that the vibration signals collected at different speeds and loads are inherently different. In addition, there are
a number of mixed fault types in this task, such as mixed inner and outer ring faults and both bearing and
gearbox faults, which can affect the final transfer results. It is worth noting that the JMMD method, which
performs quite effectively in the first two tasks, differs from the best results by around 7-8% on this task. Since
the data distribution is complex and varies significantly, domain adaptation strategies alone are not sufficient
to align the distribution well enough to achieve good diagnostic performance.

On the one hand, domain adaptation is performed at the feature extraction and classification layers via JMMD
by exploiting the differences in the joint distribution. On the other hand, adversarial domain training is per-
formed by adjusting the joint distribution to reduce domain drift. These two modules achieve maximum
category differentiation and domain adaptation in multimodal conditions. Finally, the advantages and disad-
vantages of the diagnostic approaches are verified in two cases: using the original time domain signal directly
as inputs versus transforming the data into the frequency domain and using that as inputs. It turns out that in
this task, the time domain signal is used directly as input to obtain better diagnostic results. The reason for this
phenomenon may be that the time-domain representation is more capable of intuitively reflecting the ampli-
tude, frequency, andphaseinformationofthesignalovertimeandcanbetterdisplaythewaveformshapeofthe
signal, which is very helpful for detecting short-term signal changes and analyzing signal shape. Additionally,