A New Lightweight Fault Diagnosis Framework Towards Variable Speed Rolling Bearings

Under the variable speed condition, the rolling bearing vibration signal shows non-smooth characteristics such as frequency modulation and amplitude modulation, which makes it difficult to effectively extract the fault characteristics by relying on the analysis method of traditional signals based on the smooth assumption, as well as the excessive speed change and the excitation response caused by the failure of the equipment components will lead to the change of the detected vibration signal. In this paper, a lightweight fault diagnosis framework called variable speed fault diagnosis framework (VSFD-Net) is proposed to address the above challenges. Firstly, a separable multi-scale convolutional block is proposed to achieve the extraction of multi-local sensory field features, thus reducing the learning parameters and computational effort during the training process. Secondly, the development of a broadcast self-concern block is introduced to achieve capturing the critical fine-grained global extent of the signal while avoiding complex operations such as matrix multiplication and multi-dimensional exponentials. Through experiments on three mechanical systems, it is demonstrated that the framework has significant advantages over existing neural network models, possessing lightweight and robustness.