振动信号中的周期性脉冲对于轴向柱塞泵故障诊断具有重要意义，但在工作状态下，轴向柱塞泵的振动信号经常会受到背景噪声和柱塞往复运动引起的自然周期性脉冲的污染，故障特征提取是轴向柱塞泵故障诊断的一个难点。为解决这个问题，提出基于增强聚类分割与L-峭度的Teager能量算子解调方法。与传统的聚类分割方法不同，增强后的算法是一种两周期的方法，能够有效从背景噪声和自然周期性脉冲中提取故障特征。L-峭度在识别周期性脉冲方面与峭度类似，但不像峭度对离群值那么敏感。Teager能量算子解调计算简便，比传统的希尔伯特解调更适合用来进行故障特征提取。为说明该方法的可行性，进行仿真模拟和试验数据研究，并将结果与传统的聚类分割方法进行了比较。结果表明，该方法能够有效地检测轴向柱塞泵的缸体和轴承故障。
Periodic impulses in vibration signals are useful to the detection of faults in axial piston pumps. However, in the working condition, the vibration signals of axial piston pump are often contaminated by heavy background noises and natural periodic impulses caused by the reciprocating movement of pistons. Therefore, extracting fault features is one of the most difficult tasks to identify faults in axial piston pumps. To solve this problem, the Teager energy operator(TEO) demodulation using improved clustering-based segmentation and L-Kurtosis method is proposed. Unlike the traditional clustering-based segmentation method, the improved version is a two-cycle one,it can extract the fault features out of the background noise and nature periodic impulse efficiently. L-Kurtosis is similar to kurtosis and easy to recognize impulses but is not like kurtosis to be sensitive to the outliers. The TEO demodulation is more suitable to extract faults than the traditional Hilbert demodulation, because the calculation of TEO is very simple. To illustrate the feasibility and performance of the present method, simulations and experimental data investigations are performed and the results are compared with the traditional clustering-based segmentation method. The results show that the proposed method enables the efficient detect cylinder fault and bearing fault in axial piston pumps.
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