Bienefeld, Christoph ; Becker-Dombrowsky, Florian Michael ; Shatri, Etnik ; Kirchner, Eckhard (2023)
Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis.
In: Entropy, 2023, 25 (9)
doi: 10.26083/tuprints-00024494
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning methods are becoming increasingly popular, aiming for accurate predictions of bearing faults without requiring bearing-specific domain knowledge. Opposing this trend in popularity, the present paper takes a more traditional approach, incorporating domain knowledge by evaluating a variety of feature engineering methods in combination with a random forest classifier. For a comprehensive feature engineering study, a total of 42 mathematical feature formulas are combined with the preprocessing methods of envelope analysis, empirical mode decomposition, wavelet transforms, and frequency band separations. While each single processing method and feature formula is known from the literature, the presented paper contributes to the body of knowledge by investigating novel series connections of processing methods and feature formulas. Using the CWRU bearing fault data for performance evaluation, feature calculation based on the processing method of frequency band separation leads to particularly high prediction accuracies, while at the same time being very efficient in terms of low computational effort. Additionally, in comparison with deep learning approaches, the proposed feature engineering method provides excellent accuracies and enables explainability.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Bienefeld, Christoph ; Becker-Dombrowsky, Florian Michael ; Shatri, Etnik ; Kirchner, Eckhard |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis |
| Sprache: | Englisch |
| Publikationsjahr: | 24 November 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2023 |
| Ort der Erstveröffentlichung: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Entropy |
| Jahrgang/Volume einer Zeitschrift: | 25 |
| (Heft-)Nummer: | 9 |
| Kollation: | 15 Seiten |
| DOI: | 10.26083/tuprints-00024494 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/24494 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | The engineering challenge of rolling bearing condition monitoring has led to a large number of method developments over the past few years. Most commonly, vibration measurement data are used for fault diagnosis using machine learning algorithms. In current research, purely data-driven deep learning methods are becoming increasingly popular, aiming for accurate predictions of bearing faults without requiring bearing-specific domain knowledge. Opposing this trend in popularity, the present paper takes a more traditional approach, incorporating domain knowledge by evaluating a variety of feature engineering methods in combination with a random forest classifier. For a comprehensive feature engineering study, a total of 42 mathematical feature formulas are combined with the preprocessing methods of envelope analysis, empirical mode decomposition, wavelet transforms, and frequency band separations. While each single processing method and feature formula is known from the literature, the presented paper contributes to the body of knowledge by investigating novel series connections of processing methods and feature formulas. Using the CWRU bearing fault data for performance evaluation, feature calculation based on the processing method of frequency band separation leads to particularly high prediction accuracies, while at the same time being very efficient in terms of low computational effort. Additionally, in comparison with deep learning approaches, the proposed feature engineering method provides excellent accuracies and enables explainability. |
| Freie Schlagworte: | bearing fault diagnosis, feature engineering, machine learning, condition monitoring, frequency band separation |
| ID-Nummer: | 1278 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-244945 |
| Zusätzliche Informationen: | This article belongs to the Special Issue New Trends in Fault Diagnosis and Prognosis for Engineering Applications: From Signal Processing to Machine Learning and Deep Learning |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet Produktentwicklung und Maschinenelemente (pmd) |
| Hinterlegungsdatum: | 24 Nov 2023 13:20 |
| Letzte Änderung: | 27 Nov 2023 07:14 |
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| Suche nach Titel in: | TUfind oder in Google |
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