多尺度熵鋸齒量化之馬達異常偵測

Motor Abnormal Detection Using Zigzag Quantization of Multiscale Entropy

鄧至凱(Chih-Kai Teng)

指導教授 ： 劉俞志

元智大學/資訊學院/資訊管理學系/碩士(2011年)

https://doi.org/10.6838/YZU.2011.00091

摘要

本研究之目的在於馬達異常偵測。在資料方面係使用多台噴射泵與循環抽水泵的馬達震動訊號，並用於相關建模演算法的實驗中。資料收集方式有兩種：一為直接存在嵌入式系統上，再由人工定期進行轉存到資料庫中；一為直接透過無線網路將訊號直接存到資料庫中。另外，本研究提出以鋸齒度量化的方式，以較少的屬性值取代原本的多尺度熵屬性值。由於屬性過多可能會造成嵌入式系統資源的不足，透過維度縮減可降低建模工作對系統資源的需求。

關鍵字

多尺度熵 ； 馬達異常偵測 ； 屬性篩選

並列摘要

The vibrational signals of several motors for jet pumps and circulation pumps have been collected and used in the experiment in the research. The data are collected using one of the two processes. The first process stores the signals in the embedded system, and the signals are then manual moved to the database in batches. The second process uses wireless sensors to collect and transmit the signals directly to the database. Additionally, a dimension reduction method has been proposed by quantifing the degree of sawtooth of the MSE curves. Using too many attritubes incurs much overhead on the embedded system. By replacing the MSE attributes with the sawtooth attributes, fewer attributes are needed when building the classifier for abnormal detection.

並列關鍵字

multi-scale entropy ； motor abnormality detection ； feature selection

參考文獻

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