用遗传编程方法提取和优化机械故障的声音特征

Extracting and Optimizing Sound Features in Mechanical Fault Diagnosis Using Genetic Programming

为了弥补单一特征在机械故障特征识别中的不足,引进遗传编程方法对单一特征重新优化,构建复合特征.利用声音信号的复合特征实现滚动轴承的状态识别,取得了良好效果;进一步尝试利用信息融合思想,融合机器声音和振动两种信号的单一特征,再利用遗传编程得到新的复合特征,其识别效果比单独使用声音信号复合特征更好,并且缩短了遗传编程的搜索时间.

A fault detection method is introduced, which uses compound features optimized by genetic programming based on single features. Some single features can be combined to form a compound feature. A compound feature obtained from sound signals is used to diagnose faults of rolling bearings, and its effectiveness is verified in practice. Based on the method, a new method is presented, which is improved by the information fusion technique. The features from sound signals and vibration signals are combined, and a new compound feature can be obtained by genetic programming. This feature can be used to detect faults of rolling bearings with higher efficiency and reliability.