부산대학교 도서관

The development of artificial intelligence has brought new opportunities and challenges in the field of mechanical fault diagnosis. Especially, data-driven intelligent fault diagnosis (DIFD) methods are favored by researchers. However, commonly used DIFD methods can only recognize the fault of a known class instead of an unknown class, which results in the DIFD’s inability to self-learning. To handle this problem, novel probability confidence convolutional neural network (PCCNN) is proposed in this article. The probability of belonging to each known class calculated by the PCCNN and the confidence of each known class is used to recognize the known and unknown classes. At the same time, the ability to recognize a new class is achieved by updating the architecture and parameters of the PCCNN model with a self-learning method. The bearing experiment dataset, the gearbox experiment dataset, and the rotating machinery failure data are used in the case studies. The study results show that the proposed method achieves an average accuracy of 97.42% and 96.87% for recognizing unknown and known classes, respectively, exhibiting at least 5.18% better performance than the 12 comparison methods. All the code and experimental data are available on the website. [ABSTRACT FROM AUTHOR]