Predictive Diagnosis in Axial Piston Pumps

Abstract

Increasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the consideration of variable operating conditions in the data acquisition process as well as a versatile, non domain-specific feature extraction technique. By doing so, we train anomaly detection models for different fault types and different fault intensities in variable displacement axial piston pumps. Our specific interest points to the investigation of high-frequency condition indicators with a sampling rate of 1 MHz. Furthermore, we compare those to industry standard sensors, sampled with up to 20 kHz.By considering variable operating conditions, we are able to quantify the influence of the operating point. The results show, that high-frequency features are a suitable condition-indicator across several operating points and can be used to detect faults more easily. Although set up on a test-bench, the experimental design allows to draw conclusions about realistic field operational conditions.