Dense Connection Network With Depthwise Separable Convolution
Most of the existing intelligent fault diagnosis models are suitable for only a type of rotating machine or equipment. To achieve the intelligent fault diagnosis for various rotating machines, it is significant for constructing a diagnostic model with a powerful generalization ability. Thereupon, this work addresses to explore a large fault diagnosis model for a variety of rotary machines. To process the big data from a number of rotating machines and mine their fault characteristics effectively, a dense connection network with depthwise separable convolution (DCNDSC) is proposed as the large model. In this network, a dense connection with depthwise separable convolution block (DCDSCB) is designed for representing the complex vibration data and suppressing the over-fitting, and then a series of DCDSCBs are stacked, so that DCNDSC can well extract various complicated characteristics caused by different faults and working conditions. A large rotating machine dataset including almost all public rotating machine data and our private data are built to train the large model. For enhancing the diagnostic ability of large model on the new monitoring data, a diminutive network fine-tuning strategy is proposed, while the main feature extraction capability of the pre-trained DCNDSC is preserved. Ten fault datasets are applied to verify the high accuracy and strong generalization ability of the developed large model. This model is not only effectively applied to the fault diagnosis of actual rotating machinery, but also firstly provides a pre-training large model for the field of mechanical fault diagnosis.
For the detail,please see: Yi Qin, Taisheng Zhang, Quan Qian, Yongfang Mao. Large model for rotating machine fault diagnosis based on a dense connection network with depthwise separable convolution, IEEE Transactions on Instrumentation and Measurement, 2024.