Hydraulic pump is the heart of the hydraulic system, the fault diagnosis of hydraulic system is an important part of fault diagnosis. Due to the compressibility of the fluid, the fluid-structure interaction between the pump source and the servo system and the intrinsic mechanical vibration of the hydraulic pump itself make the mechanism of the hydraulic pump complicated, the extraction of the fault feature difficult and the fuzziness of the fault diagnosis. A large number of hydraulic pump fault diagnosis data show that the fault signal detected by the pump outlet is often submerged by the interference signal, and the single fault detection signal often shows strong ambiguity. It is difficult to improve the effective fault characteristics by the conventional signal processing method. Aiming at the failure mode of piston pump ball loosening, the vibration sensor and pressure sensor are installed at the outlet of the hydraulic pump to detect the fault, the signal is denoised by wavelet analysis, the effective fusion information is extracted by principal component analysis, the BP neural network with improved algorithm To achieve the hydraulic pump weak signal or more effective fault diagnosis. 1, hydraulic pump ball loose failure mechanism analysis Due to manufacturing error or pressure pump in the course of the work of the pressure impact, often make the plunger ball and socket Shen concave deformation of the ball head and ball socket gap, resulting in the plunger Loose ball failure. 2, the wavelet signal denoising The working environment of the hydraulic pump is generally harsh, its working conditions are greatly affected by the environment, usually detected at the pump signal contains a lot of noise. The test shows that the pressure signal and the vibration signal detected at the outlet of the hydraulic pump show the following characteristics: â‘ The spectrum of the signal is very wide, the waveform is disorderly and the regularity is poor; â‘¡ The time-varying and non-stationary performance is obvious. Therefore, it is very difficult to extract fault features based on these two signals, so it is necessary to de-noising the detected signals. Wavelet analysis is a more effective method of signal processing. It can analyze signals in both time and frequency domain, and can effectively distinguish the abrupt part and noise in the signal to realize the signal denoising. Pump outlet vibration signal and its wavelet denoised signal, select the wavelet denoising global threshold of 1.049. Obviously, many interference signals are contained in the detection signal, so it is difficult to simply use the detected vibration signal for effective fault diagnosis. In order to eliminate the influence of interference, the wavelet processing can effectively eliminate the noise contained in the vibration signal at the pump outlet, which is good for the extraction of fault features. 3, information fusion fault diagnosis method Information fusion is the intelligent synthesis of multi-source information, resulting in more accurate than a single source of information, fault tolerance and robustness of the stronger estimates and judgments '2'. Because the information detected at the outlet of the hydraulic pump is weak and easily inundated by disturbance, it is difficult to effectively detect the weak fault feature by using the detection signal of a single sensor. In the information fusion fault diagnosis process, the vibration signal and pressure signal are denoised by wavelet, the effective feature information is extracted by statistical analysis, the principal component analysis is used to effectively decouple the correlation between each fault feature, and the dimension of fault feature is reduced. Using BP neural network with improved algorithm to realize hydraulic pump ball looseness fault diagnosis.
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