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Intelligent fault detection and classification is a cornerstone of prognostic and health management of rotating machinery research. Correctly classifying and predicting rotating machinery faults not only increases productivity in industrial plants, but also reduces maintenance costs. The datasets from real facilities needed to train fault classifiers often have few samples due to the expense of provoking faults in real scenarios to obtain data. This paper proposes the use of the Tabular Prior-Data Fitted Network (TabPFN) model for the classification of faults in rotating machinery. TabPFN is a model which has been pre-trained with a large amount of synthetic data with many causal relationships. This allows the model to perform Bayesian inference on the data used for training. The advantages of this model are its ability to be trained with limited data without generating overfitting problems and its high speed (if a GPU is available). To compare its performance with traditional algorithms for tabular classification such as XGboost and Random Forest, three public datasets were used. Results show that TabPFN performs more accurately than algorithms with limited data, so it is suitable to be deployed in real scenarios when the amount of data available from the monitored rotating machinery is limited.

Early fault classification in rotating machinery with limited data using TabPFN