基于量子遗传优化的原子分解算法及其在机械故障诊断中的应用

发布时间：2018-07-02 12:31

本文选题：故障诊断 + 正交匹配追踪　；参考：《机械设计与制造》2017年07期

【摘要】：机械设备故障的发生往往伴随着振动现象,通过对故障振动信号进行有效的分析是机械设备故障诊断的关键。最近提出的稀疏分解算法具有多分辨率、稀疏性和冗余的特点,但是也存在着原子库构造困难和分解算法计算量大的问题,为了更好将稀疏分解算法应用于机械故障诊断中,提出在正交匹配追踪算法的基础上,采用具有良好时频特性的Gabor原子,利用量子遗传算法快速求解多参数全局最优解的优点,从振动信号中快速和准确地提取出故障特征信息。通过数值仿真信号分析证明了所提的方法无论在特征提取的准确性上还是减小计算时间上都优于传统的正交匹配追踪算法,另外在轴承故障诊断实际应用中的实例分析中,相比传统的频谱分析方法更能有效地提取出故障特征信息,有效降低了背景噪声和杂质频率的干扰。
[Abstract]:The occurrence of mechanical equipment faults is often accompanied by vibration phenomenon. It is the key of mechanical equipment fault diagnosis through the effective analysis of fault vibration signals. The recently proposed sparse decomposition algorithm has the characteristics of multi-resolution, sparsity and redundancy, but it also has the problems of difficult construction of atomic library and large amount of computation of decomposition algorithm. In order to better apply sparse decomposition algorithm to mechanical fault diagnosis. Based on the orthogonal matching tracing algorithm, the Gabor atom with good time-frequency characteristic is adopted, and the advantages of quantum genetic algorithm (QGA) are used to solve the global optimal solution of multiple parameters quickly. The fault feature information is extracted quickly and accurately from the vibration signal. The numerical simulation signal analysis shows that the proposed method is superior to the traditional orthogonal matching tracking algorithm in the accuracy of feature extraction and the reduction of computing time. In addition, the proposed method is applied in the practical application of bearing fault diagnosis. Compared with the traditional spectrum analysis method, the fault feature information can be extracted more effectively, and the interference of background noise and impurity frequency can be effectively reduced.
【作者单位】：武汉科技大学机械自动化学院;
【基金】：国家自然科学基金(51475339) 武汉科技大学冶金装备及其控制教育部重点实验室开放基金资助项目(2015B11)
【分类号】：TH17

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