摘要
提出了一种新的衡量时间序列复杂度的方法——多尺度局部最大样本熵(Multiscale Local-maximum Sample Entropy,简称MLSE),与多尺度熵相比,MLSE抑制了振动信号中的噪声和干扰成分,同时又提高了每个时间尺度上样本熵的计算精度。将液压泵不同状态下的MLSE作为特征向量,利用可拓理论进行故障模型识别,并将其与另外两种方法进行对比,结果表明该方法故障识别准确率最高、耗时最短,验证了该方法的优越性。
A new method was proposed which is multiscale local-maximum sample entropy （MLSE） to measure the complexity of time series. The noise and interference of vibration signals were suppressed by it, at the same time, the precision of sample entropy on each time scale was improved as compared with multi-scale entropy. By treated the MLSE of hydraulic pump under different status as feature vectors, the extension theory was applied to the fault model identification, and it was compared with the other two methods. The results show that the fault identification rate of this method is the highest, and it takes the shortest time, so the superiority of this meth- od is verified.
出处
《机床与液压》
北大核心
2015年第11期182-187,共6页
Machine Tool & Hydraulics
基金
国家自然科学基金资助项目(51275524)
关键词
液压泵
模式识别
多尺度
可拓理论
Hydraulic pump
Mode identification
Muhiscale
Extension theory
