Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery.

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Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

In engineering practice, mechanical equipment is usually in polytropic working conditions, where the data distribution of training set and test set is inconsistent, resulting in insufficient generalization ability of the intelligent diagnosis model. Simultaneously, different tasks often need to be modeled separately. Domain adaptation, as one of the research contents of transfer learning, has certain advantages in solving the problem of inconsistent feature distribution. This article designs and establishes a domain adaptation framework based on multiscale mixed domain feature (DA-MMDF) for cross-domain intelligent fault diagnosis of rolling bearings under polytropic working conditions. The proposed method first uses the MMDF extractor to obtain features from the collected data, which constructs a complete feature space through variational mode decomposition (VMD) and mixed domain feature extraction to fully mine the state information and intrinsic attributes of the vibration signal. Second, the dimensionality reduction and optimization of features are achieved through extreme gradient promotion, and meaningful and sensitive features are selected according to the importance of features to eliminate redundant information. The optimized important features are combined with the manifold embedded distribution alignment method to realize the distribution alignment of data in different fields and cross-domain diagnosis. In order to verify the effectiveness of the proposed approach, the rolling bearing data sets gathered from the laboratories are employed and analyzed. The analysis result confirms that DA-MMDF is able to achieve effective transfer diagnosis between polytropic working conditions. Compared with traditional intelligent fault diagnosis methods and DA methods, the method proposed in this article achieved the state-of-the-art performances.

An Intelligent Fault Diagnosis Method Based on Domain Adaptation and Its Application for Bearings Under Polytropic Working Conditions

Accuracy-improved bearing fault diagnosis method based on AVMD theory and AWPSO-ELM model

Adaptive energy-constrained variational mode decomposition based on spectrum segmentation and its application in fault detection of rolling bearing

Intelligent fault diagnosis of diesel engine via adaptive VMD-Rihaczek distribution and graph regularized bi-directional NMF

Fault diagnosis for rolling bearing based on VMD-FRFT

The invention discloses a rolling bearing fault diagnosis method based on VMD-FRFT (Variational Mode Decomposition-Fractional Fourier Transform), and relates to the technical field of the fault diagnosis method of a bearing. The method comprises the following steps that: extracting the vibration signal of a rolling bearing, and processing the vibration signal to obtain a multi-component LMF (Linear Frequency Modulation) vibration signal; carrying out VMD-FRFT on the multi-component LMF vibration signal to obtain the modal component of the vibration signal; according to the obtained modal component of the vibration signal, reconstructing the vibration signal; and carrying out wavelet transform on the reconstructed vibration signal to obtain an instantaneous frequency conversion curve, and according to the instantaneous frequency conversion curve, obtaining the fault information of the rolling bearing. The method which is put forward is successfully applied to rolling bearing inner ringfault data analysis, instantaneous fault characteristic frequency can be accurately extracted, and noise robustness is high.

Rolling bearing fault diagnosis method based on VMD-FRFT (Variational Mode Decomposition-Fractional Fourier Transform)

The invention, which relates to the technical field of fault signal diagnosis methods for bearings, discloses a bearing fault diagnosis method based on a cross-correlation singular value. The method comprises the following steps that: singular value decomposition is carried out on a bearing vibration signal after phase space reconstruction to obtain singular value component signals of a bearing vibration signal; two singular value component signals are selected to carry out cross correlation, denoising is carried out on the signals and fault characteristics are highlighted based on the characteristics of denoising and same-frequency signal highlighting of the cross-correlation function, so that a cross-correlation time-domain signal is obtained; and envelope demodulation is carried out ona zone with the obvious impact characteristic in a cross-correlation time domain signal and a fault characteristic frequency of the bearing vibration signal is extracted. With the method, a problem ofhigh frequency doubling characteristic missing in the strong noise background is solved; the bearing fault characteristic frequency is extracted accurately; and the high frequency doubling is obtained precisely.

Li Xiang

Papers

Fault diagnosis for rolling bearing based on VMD-FRFT

Rolling bearing fault diagnosis method based on VMD-FRFT (Variational Mode Decomposition-Fractional Fourier Transform)

Bearing fault diagnosis method based on cross-correlation singular value