Gear pitting is a common gear fault, which has been an important subject to industry and research community, In the past, the diagnosis of gear pitting faults was all based on fixed operating conditions and the fixed gear health state, which is a in-set detection, However, in real industrial scenarios, gear pitting fault diagnosis is always an open-set detection, in which the working conditions and the gear health state are commonly not known in advance. In order to deal with this open-set detection problem, we proposed a three-stage diagnosis method. In the first stage, we built an in-set health state classification model based on Domain2Vec to solve the domain generalization problem caused by different operating conditions. In the second stage, we modify the classification model to a regression model to predict the out-of-set health state sample in the dataset. In the third stage, we used KNN algorithm to correct the wrong model in the second stage and further improve the accuracy of classification. Our proposed method achieved scores of 463.5 and 472 on the test set and validation set, respectively, and ranked first in the 2023 PHM Conference Data Chanllenge.
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Fault Diagnosis, Data Challenge
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