A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN. (15th August 2022)

• Record Type: Journal Article
• Authors: Gu, Jun; Peng, Yuxing; Lu, Hao; Chang, Xiangdong; Chen, Guoan
• Is Part Of: Measurement. Volume 200(2022)
• Publication Date: 2022-08-15
• Subjects: Fault diagnosis -- Variational mode decomposition -- Convolutional neural networks -- Support vector machine -- Rolling bearings; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111635
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A hybrid model based on VMD, CNN and SVM was proposed for rolling bearings diagnosis. The performance of VMD on an analog signal with DC component and noise is studied. Overlap sampling for one-dimensional data reconstructed from IMF components. The validity of the method is verified by different samples and methods. Abstract: The rolling bearings play a vital role in mechanical production and transportation. However, when it appears abnormal, the fault characteristics are weak and different to be extracted in vibration signals which generally include pulse information reflecting fault type, independent vibration components caused by other normal mechanical components, noise in the surrounding environment, and so on. A hybrid method for rolling bearing fault diagnosis based on variational modal decomposition (VMD), continuous wavelet transform (CWT), convolutional neural network (CNN) and support vector machine (SVM) suitable for processing small samples is proposed. The pre-processed data are first subjected to VMD processing, and then the IMF reconstructed data are overlapped and sampled to obtain a two-dimensional time-frequency image using CWT. Then, a CNN model is constructed with selected hyperparameters, and image training samples are input into the CNN for model training. Finally, the pre-trained CNN model is selected to learn the test sample set layer by layer to realize the extraction of fault features and use SVM instead of Softmax function to identify and classify the faults. The effectiveness of the proposed method is verified with the Case Western Reserve University (CWRU) bearing vibration data and spindle device failure test bench dataset, where the classification accuracy of the former averaged 99.9% and that of the latter averaged 90.15%. The results validate the feasibility of the method and outperform traditional CNN models, feature extraction-based methods and other methods in terms of diagnostic accuracy.