The invention provides an autonomous underwater robot propeller fault feature extraction method, which is specifically based on empirical mode decomposition, fractal dimension and an SHFC positioningalgorithm, and belongs to the technical field of underwater robot fault diagnosis. According to the invention, empirical mode decomposition is adopted to carry out data preprocessing to replace a common filtering method in fractal dimensions; in a high-frequency part after modal decomposition, a rolling time window is introduced, through extraction of fractal dimension fault features of a small sample in each time window, sudden change of a moment fractal dimension feature value from a fault to a fractal dimension is captured, a maximum value of the sudden change of the fractal dimension is extracted, and then an extraction effect of the fault features is enhanced. According to the invention, fault characteristics can be enhanced, whether the underwater robot propeller has faults can be detected conveniently, and the method is especially suitable for state monitoring of autonomous underwater robot propellers.
本发明提供一种自主式水下机器人推进器故障特征提取方法,具体是基于经验模态分解、分形维数和SHFC定位算法的,属于水下机器人故障诊断技术领域。该方法采用经验模态分解进行数据预处理替代分形维数中普通的滤波方法;在模态分解后的高频部分,把滚动时间窗引入,通过每个时间窗内小样本的分形维数故障特征的提取,捕捉到故障到分形维数中发生时刻分形维数特征值的突变,提取出分形维数突变的极大值,进而增强故障特征的提取效果。本发明专利能增强故障特征,便于检测水下机器人推进器是否出现故障,特别适合应用于自主式水下机器人推进器的状态监测。
Autonomous underwater robot propeller fault feature extraction method
一种自主式水下机器人推进器故障特征提取方法
2020-05-12
Patent
Elektronische Ressource
Chinesisch
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