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Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor

机译：自适应小波变换在振动信号建模中的应用

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摘要

There has been an increasing application of water hydraulics in industries due to growing concern on the environmental, health and safety issues. The fault diagnosis of water hydraulic motor is important for improving water hydraulic system reliability and performance. In this paper, fault diagnosis of water hydraulic motor in water hydraulic system is investigated based on adaptive wavelet analysis. A novel method for modelling the vibration signal based on the adaptive wavelet transform (AWT) is proposed. The linear combination of wavelets is introduced as wavelet itself and adapted for the particular vibration signal, which goes beyond adapting parameters of a fixed-shape wavelet. The AWT procedure based on the parametric optimisation by genetic algorithm (GA) is developed. The model-based method by AWT is applied to extract the features in the fault diagnosis of the water hydraulic motor. This technique for de-noising the corrupted simulation signal shows that it can improve the signal-to-noise ratio of the vibration signal. The results of the experimental signal demonstrate the characteristic vibration signal details in fine resolution. The magnitude plots of the continuous wavelet transform (CWT) show the characteristic signal's energy in time and frequency domain which can be used as feature values for fault diagnosis of water hydraulic motor.
机译：由于对环境，健康和安全问题的日益关注，水液压技术在工业中的应用越来越多。水压马达的故障诊断对于提高水压系统的可靠性和性能至关重要。本文在自适应小波分析的基础上，研究了水压系统中水压电动机的故障诊断方法。提出了一种基于自适应小波变换（AWT）的振动信号建模方法。小波的线性组合作为小波本身被引入，并且适用于特定的振动信号，这超出了固定形状小波的自适应参数。开发了基于遗传算法参数优化的AWT程序。运用AWT的基于模型的方法提取水压马达故障诊断的特征。这种对损坏的模拟信号进行降噪的技术表明，它可以提高振动信号的信噪比。实验信号的结果以高分辨率显示了特征振动信号的细节。连续小波变换（CWT）的幅值图显示了特征信号在时域和频域的能量，可用作水力液压马达故障诊断的特征值。

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