An effective method for fault diagnosis of rotating machinery under noisy environment

Abstract

Abstract Rotating machinery is widely utilized as mechanical equipment in the industrial field. However, due to the complex working conditions, the existing fault diagnosis methods have failed to address good results in practical applications. To improve the fault diagnosis performance of rotating machinery in a noisy environment, a new multi-scale convolution neural network (MSCN) based on a self-calibrating attention module is proposed. First, this thesis constructs a multi-scale convolution layer with a wide convolution kernel to form an efficient sampling structure at the filter level, which can filter out incoherent noise from the signals and extract rich features. Second, a multi-scale self-calibrating attention module is implemented with two identical self-calibrating convolutional networks to continuously focus on significant embeddings and adaptively combine information from different spatial dimensions. Third, the multi-dimensional characteristics are integrated by the feature cascade layer, and then the fault modes are identified though the classifier layer under noise. Finally, based on the Case Western Reserve University datasets and Paderborn University bearing datasets, the experimental results show that our proposed MSCN can significantly enhance the fault identification ability to rotate machinery in a noisy environment.