液压泵健康状态评估方法研究

发布时间：2018-07-06 08:25

本文选题：健康状态评估 + 轴向柱塞泵　；参考：《燕山大学》2012年硕士论文

【摘要】：随着我国制造业装备水平的不断提高，机械设备也越来越先进，对这些设备自动化程度和水平的要求越来越高，如果这些关系到国计民生的大型和关键设备出现了故障，就会造成不同程度的经济损失。因此，对于机械设备进行健康状态评估，实时地对其工作状态进行监测，在设备运转过程中把握设备的工作状态是非常有必要的。通过传感器采集斜盘式轴向柱塞泵的泵端盖振动信号，运用小波包技术对采集到的信号进行分解和重构来实现带通滤波，得到故障信号所在的频段。利用小波包消噪方法对该频段进行消噪处理，，提高信号的信噪比，突出故障特征。然后采用Hilbert包络解调技术对消噪的信号进行包络解调，将故障信号从共振信号中解调出来。再对此信号进行降频处理，得到0~2000Hz的频带范围内，得到一个含有丰富故障信息的信号。最后，再利用该信号进行健康状态特征提取。信息熵描述了系统状态不确定性的程度和复杂程度，即信源输入的信息越不稳定和越复杂，信息熵越大。通过对大量资料的查阅，得出信息熵在故障程度评估方面有非常好的效果。采集不同磨损程度的滑靴工作时的振动信号，在时域、频域和时频域对该振动信号进行信息熵的特征提取，得到奇异谱熵、功率谱熵、小波系数能量熵和小波包能量熵，经过实验及数据分析可以得出健康状态越恶劣，信号的复杂度和不确定度越高，那么信息熵值也就越大。所以，信息熵是一个非常好的评估指标，可以应用到液压泵健康状态的评估中。
[Abstract]:With the continuous improvement of the equipment level of our manufacturing industry, the mechanical equipment is becoming more and more advanced, and the requirements for the automation degree and level of these equipment are becoming higher and higher. If these large-scale and key equipment related to the national economy and the people's livelihood are broken down, Will cause varying degrees of economic losses. Therefore, it is very necessary to evaluate the health status of mechanical equipment and monitor its working state in real time, so as to grasp the working state of the equipment in the process of operation. The vibration signal of the end cover of the axial piston pump with oblique disc is collected by the sensor, and the signal is decomposed and reconstructed by wavelet packet technology to realize the bandpass filtering, and the frequency band of the fault signal is obtained. The wavelet packet denoising method is used to de-noise the frequency band to improve the signal-to-noise ratio of the signal and to highlight the fault characteristics. Then the signal is demodulated by Hilbert envelope demodulation technique, and the fault signal is demodulated from the resonance signal. Then the frequency of the signal is reduced and a signal with abundant fault information is obtained in the frequency range of 0 ~ 2000Hz. Finally, the signal is used to extract the health state feature. Information entropy describes the degree and complexity of system state uncertainty, that is, the more unstable and complex the input information is, the greater the information entropy is. By consulting a large number of data, it is concluded that information entropy has a very good effect in fault degree evaluation. The vibration signals of sliding boots with different wear degrees are collected, and the information entropy of the vibration signal is extracted in time domain, frequency domain and time frequency domain. The singular spectrum entropy, power spectrum entropy, wavelet coefficient energy entropy and wavelet packet energy entropy are obtained. Through experiments and data analysis, it can be concluded that the worse the health condition is, the higher the complexity and uncertainty of the signal is, and the greater the information entropy is. Therefore, information entropy is a very good evaluation index, which can be applied to the evaluation of hydraulic pump health.
【学位授予单位】：燕山大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH137.51

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