Abstract
Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.
Highlights
Large-scale intelligent production processes are becoming more and more complicated, as they are closely connected with each other
Thismultimodal section is divided into three parts to introduce the differential geometric feature fusion-based deep neuralnetwork network (DNN) (DGFFDNN)-based fault diagnosis method: differential feature extraction, multimodal feature fusion, online and real-time online method: multimodal differential feature extraction, multimodal feature fusion, and real-time online diagnosis of the frequency-type fault
A simulation study and a bearing case study were both illustrated to validate the efficiency of the DGFFDNN-based fault diagnosis method
Summary
Large-scale intelligent production processes are becoming more and more complicated, as they are closely connected with each other. In the most recent three decades, research on fault diagnosis of key equipment in intelligent production processes has attracted extensive attention from academic and engineering researchers [6,9,10,11,12,13]. Three classes of fault diagnosis methods are developed: fault diagnosis methods based on a physical model, a fault diagnosis method based on knowledge, and a data-driven-based method. Precise physical model requirements limit its application in the field of fault diagnosis for complex mechanical equipment, and the processing of a quantity of prior knowledge limits its inference validation.
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