Abstract
An axial piston pump fault diagnosis algorithm based on empirical wavelet transform (EWT) and one-dimensional convolutional neural network (1D-CNN) is presented. The fault vibration signals and pressure signals of axial piston pump are taken as the analysis objects. Firstly, the original signals are decomposed by EWT, and each signal component is screened and reconstructed according to the energy characteristics. Then, the time-domain features and the frequency-domain features of the denoised signal are extracted, and features of time domain and frequency domain are fused. Finally, the 1D-CNN model was deployed to the WISE-Platform as a Service (WISE-PaaS) cloud platform to realize the real-time fault diagnosis of axial piston pump based on the cloud platform. Compared with ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD), the results show that the axial piston pump fault diagnosis algorithm based on EWT and 1D-CNN has higher fault identification accuracy.
Highlights
With the development of industry modernization and information technology, hydraulic technology is more and more widely used in all walks of life
The fault diagnosis problems of hydraulic systems are mainly concentrated in three aspects: first of all, the original signal of the hydraulic system is mixed with a lot of noise signals; secondly, there is a lack of fault diagnosis models with good generalization ability; thirdly, there is a lack of a unified solution for device management and fault diagnosis based on the cloud platform
Vibration signal processing has always been the research hotspot of signal processing methods, which is very important for equipment vibration signal detection and fault diagnosis [6,7,8]. ere are two main types of vibration signal processing methods: one is traditional methods such as amplitude domain analysis [9], Fourier transform [10], and other methods; and the other is more modern methods, such as Wigner-Ville distribution [11], spectrum analysis [12], and wavelet analysis [13]. e current vibration signal decomposition methods mainly include wavelet packet decomposition, Ensemble Empirical Mode Decomposition (EEMD), Complementary Ensemble Empirical Mode
Summary
With the development of industry modernization and information technology, hydraulic technology is more and more widely used in all walks of life. In order to reduce the maintenance cost of hydraulic equipment and realize the real-time monitoring and fault diagnosis of the working status of the axial piston pump, this paper proposes a hydraulic pump fault diagnosis method based on deep learning and cloud platform. 2. Signal Denoising Algorithm Based on Empirical Wavelet Transform e core of EWT is adaptive segmentation of the Fourier spectrum of the original signal. In order to solve the problem that hydraulic pump vibration signal is easy to be interfered by noise, a combined noise reduction method based on EWT and energy index is proposed. In order to be able to quantitatively analyze the effects of the three signal denoising methods, this paper compares the SNR of the three denoising signals, as shown in Table 1. e SNR of the signal denoising method based on EWT is obviously higher than the other two methods; it proves the effectiveness of the EWT-based signal denoising method proposed in this paper
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