Abstract
This paper constructs a novel network structure (SVD-1DCNN) based on singular value decomposition (SVD) and one-dimensional convolutional neural network (1DCNN), which takes the original signal as input to realize intelligent diagnosis of bearing faults. The output of the first convolution layer was also analyzed from the perspectives of time domain and time-frequency domain in the simulation experiment. Through qualitative analysis and quantitative analysis, it was found that the convolution kernel not only extracted the classification features of signals but also gradually highlighted the learned features in the network training process. Moreover, applying this network in fault diagnosis of bearing date provided by the Case Western Reserve University (CWRU) Bearing Data Center, it was found that the convolution kernel could also achieve the above operation. The novel network of this paper achieved a good classification effect on both the simulated signals and the measured signals.
Highlights
A small fault in a mechanical device often affects the stability and safety of the entire system and can even lead to catastrophic consequences [1]
E collected data include vibration signal, acoustic signal, and temperature signal, and since the vibration signal can directly characterize the state of the mechanical equipment, the vibration signal is most commonly collected in fault diagnosis [3]
Wang et al [11] used KPCA to extract features from bearing fault signal and used k-nearest neighbor (KNN) as a classifier to achieve diagnosis; Fei et al [12] reconstructed the characteristics of bearing vibration signal after singular value decomposition based on wavelet packet transform phase space and established support vector machine (SVM) model of bearing diagnosis; Mahamad and Hiyama [13] performed fast Fourier transform (FFT) and envelope processing on the bearing vibration signal, extracted time domain and frequency domain feature as input, and used ANN to fulfill the diagnosis
Summary
A small fault in a mechanical device often affects the stability and safety of the entire system and can even lead to catastrophic consequences [1]. The form of input signal becomes more diversified, and its objectivity and accuracy may be affected if feature extraction is still based on past experience [14, 15]; second, the feature extraction methods are poor in generality, and often a method only has a good feature extraction result for a certain type of signal; and third, feature extraction and pattern recognition are two independent processes, and the diagnosis model cannot be jointly optimized globally. It is impossible to determine whether the removed signal components contain the classification features required by the network, and the process of denoising and network extraction is two independent processes Another way of thinking is to reduce the influence of man-made, directly using the original signal as input, and complete feature extraction and pattern recognition through 1DCNN. E rest of the paper is organized as follows: Section 2 briefly describes SVD-DCNN, Section 3 performs simulation experiment, Section 4 uses the proposed method for bearing fault diagnosis and verifies the effectiveness and feasibility of the method, and Section 5 presents the conclusions
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