Abstract
Center spring wear faults in hydraulic pumps can cause fluid pressure fluctuations at the outlet, and the fault feature information on fluctuations is often contaminated by different types of fluid flow interferences. Aiming to resolve the above problems, a fluid pressure signal method for hydraulic pumps based on Autogram was applied to extract the fault feature information. Firstly, maximal overlap discrete wavelet packet transform (MODWPT) was adopted to decompose the contaminated fault pressure signal of center spring wear. Secondly, based on the squared envelope of each node, three kinds of kurtosis of unbiased autocorrelation (AC) were computed in order to describe the fault feature information comprehensively. These are known as standard Autogram, upper Autogram and lower Autogram. Then a node corresponding to the biggest kurtosis value was selected as a data source for further spectrum analysis. Lastly, the data source was processed by threshold values, and then the fault could be diagnosed based on the fluid pressure signal.
Highlights
Fluid plays an important role in the power transmission of the hydraulic system or component [1,2].Because of hydraulic impact and mechanical faults, fluid flow pressure is very complex and nonstationary in hydraulic systems or components
We firstly introduce Autogram into the fault feature extraction for the fluid pressure signal of a hydraulic pump successfully
Based on three kinds of kurtosis and threshold values, we find that only standard Autogram can select the optimal frequency band, and rich feature information of center spring wear fault can be extracted effectively without processing of the threshold value
Summary
Fluid plays an important role in the power transmission of the hydraulic system or component [1,2]. The above aforementioned noises can cause problems in the fault feature extraction of the hydraulic systems or components based on the fluid pressure signal. The denoising threshold could be self-learnt in the steady flow state, and its noise suppression effect was better than that of the traditional wavelet algorithms based on fluid pressure signals of hydraulic pipeline [14]. The method decomposed the hydraulic pressure signal using the wavelet-transform technique, and reconstructed it at the low-frequency region; the ship fault could be diagnosed effectively [16]. In 2018, the Italian scholars Ali Moshrefzadeh and Alessandro Fasana proposed a new method named Autogram based on unbiased autocorrelation (AC) [34] It was applied on rolling element bearings. We firstly introduce Autogram into the fault feature extraction for the fluid pressure signal of a hydraulic pump successfully.
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