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Predictive maintenance for test tower

  • This paper analyses an electrical test tower of the OMCIRON electronics GmbH and evaluates whether a Predictive Maintenance (PdM) strategy can be implemented for the test towers. The company OMICRON electronics GmbH performs unit tests for its devices on test towers. Those tests consist of a multitude of subtests which all return a measurement value. Those results are tracked and stored in a database. The goal is to analyze the data of the test towers subtests and evaluate the possibility of implementing a predictive maintenance system in order to be able to predict the RUL and quantify the degradation of the test tower. By assuming that the main degradation source are the relays of the test tower, a reliability modelling is performed which is the model-driven approach. The data-driven modelling process of the test tower consists of multiple steps. Firstly, the data is cleaned and compromised by removing redundances and optimizing for the best subtests where a subtest is rated as good if the trendability and monotonicity metric values are above a specific threshold. In a second step, the trend behaviours of the subtests are analyzed and ranked which illustrates that none of the subtests contained usable trend behaviour thus making an implementation of a PdM system impossible. By using the ranking, the data-driven model is compared with the reliability model which shows that the assumption of the relays being the main error source is inaccurate. An analysis of a possible anomaly detection model for a PdM is evaluated which shows that an anomaly detection is not possible for the test towers as well. The implementability of PdM for test towers and other OMICRON devices is discussed and followed up with proposals for future PdM implementations as well as additional analytical analyses that can be performed for the test towers.

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Metadaten
Author:Gabdelgaziz Sayfutdinov
DOI:https://doi.org/10.25924/opus-4215
Subtitle (English):Analysis of suitability of a predictive maintenance strategy for an electrical test tower
Advisor:Steffen Finck
Document Type:Master's Thesis
Language:English
Year of publication:2021
Publishing Institution:FH Vorarlberg (Fachhochschule Vorarlberg)
Granting Institution:FH Vorarlberg (Fachhochschule Vorarlberg)
Release Date:2021/10/06
Tag:Predictive maintenance
Number of pages:102
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
Open Access?:ja
Course of Studies:Mechatronics
Licence (German):License LogoUrhG - The Austrian Copyright Act applies - Es gilt das österr. Urheberrechtsgesetz