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Liu et al. Intell Robot 2023;3(2):131-43 I http://dx.doi.org/10.20517/ir.2023.07 Page 137

Table 2. The accuracy of different domain adaptation methods in CWRU datasets(%)

Method No-TL AdaBN MKMMD CORAL JMMD DA CDA OURS
Task 0-1 98.77 99.68 100 98.38 99.68 99.35 100 100
Task 0-2 96.49 99.48 96.43 100 100 99.03 99.68 100
Task 0-3 94.43 98.38 92.88 100 99.03 99.35 92.56 99.68
Task 1-0 97.55 95.40 98.08 98.85 100 99.23 97.32 100
Task 1-2 98.70 99.87 100 98.35 100 99.03 100 100
Task 1-3 94.82 99.03 98.71 99.68 100 99.68 100 100
Task 2-0 96.02 94.64 98.47 96.55 97.32 98.47 95.79 99.23
Task 2-1 98.18 99.29 98.05 97.08 100 99.03 96.43 100
Task 2-3 98.77 99.22 100 99.35 100 99.03 99.68 100
Task 3-0 87.82 90.04 84.67 99.23 98.08 95.79 97.70 98.85
Task 3-1 88.56 93.18 92.86 99.35 98.38 90.26 95.45 100
Task 3-2 87.98 95.32 96.10 100 99.68 97.40 98.70 100

to introducing two learnable weight matrices, 1 and 2, to unify and into the same dimension and add
them together to represent the joint distribution of features and labels.

4. EXPERIMENTAL VERIFICATION
In this section, the proposed semi-supervised joint adaptation transfer network with adversarial learning is
evaluated by examining vibration signal data from different rotary machine types, such as motor bearings,
wind turbine bearings, and gearbox bearings and gears. The three datasets were used to evaluate the diag-
nostic capability of our method under different loads, speeds, and mixed fault-type scenarios. We conducted
comparative experiments across multiple tasks using six existing transfer methods and analyzed the diagnostic
effectiveness of no migration. We then demonstrate that our proposed semi-supervised method exhibits good
diagnostic capability. This plays a crucial role in situations where obtaining fault data is difficult.

4.1. Case 1: CWRU bearing datasets under different loads
4.1.1. Data description
In this case, the bearing dataset is from the CWRU laboratory [25] . The experimental setup mainly consists of a
dependent motor, a torque sensor/encoder, and a load motor. The bearing dataset is collected at four loads (0,
1, 2, and 3 HP). Single point faults are arranged on the bearings using electrical discharge machining (EDM)
to simulate inner race faults (IF), rolling element faults (RF), and outer race faults (OF). Twelve transfer tasks
are designed by migrating between the four load states. In addition, 1000 samples of length 1024 are provided
for each data type. The sampling rate for our task is selected as 12 kHz. To obtain the diagnostic model, 80%
of the data is used, while 20% is used to verify its validity.

A dataset of bearings with variable load conditions from the CWRU laboratory is applied to illustrate that
the model could accurately classify fault types. To assess the diagnostic capability of the model, comparative
tests with some commonly used domain adaptation algorithms such as MKMMD, CORAL, JMMD, domain
adversarial (DA), and CDA are performed. In our article, average accuracy is a key indicator to evaluate the
diagnosis results of different methods.

4.1.2. Experimental results and analysis
ThecomparativeresultsoftheeightdifferentmethodsonthebearingdatasetareshowninTable2. Ourmethod
is still the best performer among the eight methods on this dataset, with an average accuracy of 100% for 9
out of the 12 migration tasks. Some other domain adaptation methods, including JMMD, have also achieved
positive results, probably because this dataset is relatively simple and the differences in the distribution are