A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

Sensors

Qin Wang¹  Meiling Zhang¹  Wenfeng Gong²  Cong Guan²  Ruihan Wang²  Zehui Zhang²  Hui Chen² 

[1] Beihai Campus, Guilin University of Electronic and Technology, Beihai 536000, China;Key Laboratory of High-Performance Ship Technology of Ministry of Education in China, School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China;

关键词: intelligent fault diagnosis;    convolutional neural network;    support vector machine;    global average pooling;    multichannel;    data fusion;    deep learning;    rotating machinery;   

DOI  :  10.3390/s19071693

来源: DOAJ

【 摘 要 】

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

【 授权许可】

Unknown