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Fault detection in linear electromagnetic actuators using time and time-frequency-domain features based on current and voltage measurements

  • In this paper an approach towards databased fault diagnosis of linear electromagnetic actuators is presented. Time and time-frequency-domain methods were applied to extract fault related features from current and voltage measurements. The resulting features were transformed to enhance class separability using either Principal Component Analysis (PCA) or Optimal Transformation. Feature selection and dimensionality reduction was performed employing a modified Fisher-ratio. Fault detection was carried out using a Support-Vector-Machine classifier trained with randomly selected data subsets. Results showed, that not only the used feature sets (time-domain/time-frequency-domain) are crucial for fault detection and classification, but also feature pre-processing. PCA transformed time-domain features allow fault detection and classification without misclassification, relying on current and voltage measurements making two sensors necessary to generate the data. Optimal transformed time-frequency-domain features allow a misclassification free result as well, but as they are calculated from current measurements only, a dedicated voltage sensor is not necessary. Using those features is a promising alternative even for detecting purely supply voltage related faults.

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Author:Christian Knöbel, Zakaria Marsil, Markus Rekla, Johannes ReuterORCiD, Clemens GühmannGND
Parent Title (English):20th International Conference on Methods and Models in Automation and Robotics (MMAR), 24-27 Aug. 2015, Miedzyzdroje, Poland
Document Type:Conference Proceeding
Year of Publication:2015
Release Date:2019/03/04
First Page:547
Last Page:552
Volltextzugriff für Angehörige der Hochschule Konstanz via IEEE Xplore möglich.
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Open Access?:Nein
Licence (English):License LogoLizenzbedingungen IEEE