Wavelet-Prototypical Network Based on Fusion of Time and Frequency Domain for Fault Diagnosis

A few-shot sample augmentation algorithm based on SCAM and DEPS for pump fault diagnosis

In recent years, pumps have become critical components in agriculture, industry, and the military, necessitating extensive development and implementation of the fault diagnosis method. In the majority of existing fault classification models, the connection between performance improvement and the amount of training data remains high, yet real-world samples are difficult to obtain. Combining domain migration theory and sample expansion method, this paper introduces a few-shot learning fault diagnosis method. Employing the T-SNE visualization algorithm, we examine the validity of the self-calibration attention mechanism (SCAM) and distribution edge prediction strategy (DEPS). The accomplishment demonstrated that the proposed algorithm could effectively map the expanded sample space within a separate interval, thereby avoiding the problem of feature aliasing caused by the overlap of sample features among similar categories and significantly enhancing the quality and quantity of training samples. The experimental analysis indicates that the proposed methodology can effectively increase the accuracy of few-shot tasks, especially in the 9way-15shot task, where it maintains a performance of 72 %, which leading the mean accuracy calculated from the others of about 30%. It is believed that much of the work has superior applicability to other few-shot diagnosis cases.

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.