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In dieser Arbeit wird ein Verfahren zur Identifikation von Schäden an Axialkolbenpumpen mit-tels breitbandiger Schadensindikatoren vorgestellt. Die Leistungsfähigkeit dieses Ansatzes wird anhand eines Datensatzes mit drei unterschiedlichen Schadenstypen mit bis zu vier Schadensstufen sowie 96 Betriebszuständen nachgewiesen. Zudem wird durch die Verwen-dung von zwei Sensortypen an jeweils drei Positionen die Übertragbarkeit des Ansatzes de-monstriert. Anhand von Daten aus einem vorab separierten Betriebsbereich der Pumpe wird gezeigt, dass die Rate der Fehlklassifikationen mit diesem Verfahren bei passender Wahl der Sensorposition, des Sensortyps und ausreichend hoher Abtastrate ca. 0,1 % beträgt.

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.

Today, very few people would likely deny the fact that data can present major added value for companies. But analyzing data from production processes reveals the incompleteness of data collection and the associated reduced potential of the data that can be leveraged. Typical shortcomings include: - Incomplete representation of processes in the dataspace, - Inadequate connection of processes and domains in the dataspace, - Lacking quality assurance during data collection, and - Insufficient or obsolete interfaces for manual data entry. The latter especially represents an obstacle not to be underestimated. Often, important process knowledge only exists implicitly in the heads of a few experts, who are only available for limited periods of time. If these people are not available at critical moments, eliminating sudden issues within processes and ensuring process reliability and stability requires much more effort. Over the long term, losing people with relevant knowledge presents significant risks for companies.