新知识检测
水准点(测量)
方位(导航)
信号(编程语言)
状态监测
计算机科学
集合(抽象数据类型)
电动机
数据集
工程类
人工智能
模式识别(心理学)
数据挖掘
控制工程
新颖性
电气工程
大地测量学
哲学
程序设计语言
地理
神学
作者
Christian Lessmeier,James Kuria Kimotho,Detmar Zimmer,Walter Sextro
标识
DOI:10.36001/phme.2016.v3i1.1577
摘要
This paper presents a benchmark data set for condition monitoring of rolling bearings in combination with an extensive description of the corresponding bearing damage, the data set generation by experiments and results of data-driven classifications used as a diagnostic method. The diagnostic method uses the motor current signal of an electromechanical drive system for bearing diagnostic. The advantage of this approach in general is that no additional sensors are required, as current measurements can be performed in existing frequency inverters. This will help to reduce the cost of future condition monitoring systems. A particular novelty of the present approach is the monitoring of damage in external bearings which are installed in the drive system but outside the electric motor. Nevertheless, the motor current signal is used as input for the detection of the damage. Moreover, a wide distribution of bearing damage is considered for the benchmark data set. The results of the classifications show that the motor current signal can be used to identify and classify bearing damage within the drive system. However, the classification accuracy is still low compared to classifications based on vibration signals. Further, dependency on properties of those bearing damage that were used for the generation of training data are observed, because training with data of artificially generated and real bearing damages lead to different accuracies. Altogether a verified and systematically generated data set is presented and published online for further research.
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