Influence of Sensor Network Sampling Rate on Multivariate Statistical Condition Monitoring of Industrial Machines and Processes

Proceedings

Klein, Steffen¹  Schneider, Tizian²  Schütze, Andreas³  Helwig, Nikolai⁴ 

[1] Author to whom correspondence should be addressed.;Lab for Measurement Technology, Department Systems Engineering, Saarland University, 66123 Saarbruecken, Germany;Presented at the Eurosensors 2018 Conference, Graz, Austria, 9–12 September 2018.;ZeMA—Centre for Mechatronics and Automation Technology, Department Sensors and Actuators, 66121 Saarbruecken, Germany

关键词: machine learning;    sampling rate;    condition monitoring;    fault classification;    remaining useful lifetime estimation;    data fusion;   

DOI  :  10.3390/proceedings2130781

学科分类：社会科学、人文和艺术（综合）

来源: mdpi

【 摘 要 】

In this paper, the deterioration of statistical fault classification of a hydraulic system and an electromechanical cylinder EMC due to reduced sampling rates of sensor nets is shown. As a result, two types of faults can be distinguished: On the one hand, degradation processes which primarily show static symptoms over the whole working cycle and, thus, are less susceptible to reduced time resolution; on the other hand, the detection of faults with symptoms localized in time, e.g., during transients, is significantly degraded. Furthermore, the EMC example shows the importance of data representation that needs to be adapted to the sampling rate.

【 授权许可】

CC BY