Fault diagnosis of rotating machinery based on recurrent neural networks

Fault diagnosis of rotating machinery is essential for maintaining system performance and ensuring the operation safety. Deep learning (DL) has been recently developed rapidly and achieved remarkable results in fault diagnosis. However, the temporal information from time-series signals is ignored by convolutional neural networks (CNNs) based methods. Besides, the robustness against the noise is essential to methods for fault diagnosis. Therefore, a novel method based on recurrent neural networks (RNNs) is proposed to identify fault types in rotating machinery in this paper. One-dimensional time-series vibration signals are first converted into two-dimensional images. Then, Gated Recurrent Unit (GRU) is introduced to exploit temporal information of time-series data and learn representative features from constructed images. A multilayer perceptron (MLP) is finally employed to implement fault recognition. Experimental results show that the proposed method achieves the best performance on two public datasets compared with existing work and exhibits the robustness against the noise.

Automated summary

A novel method based on recurrent neural networks is proposed for fault type identification in rotating machinery, utilizing one-dimensional time-series vibration signals converted into two-dimensional images, with the introduction of Gated Recurrent Unit (GRU) and multilayer perceptron (MLP) to achieve the best performance and robustness against noise compared to existing work.