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

Abstract

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.

Keywords

• machine learning
• sampling rate
• condition monitoring
• fault classification
• remaining useful lifetime estimation
• data fusion