TU Darmstadt / ULB / TUprints

Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis

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
Article, Secondary publication, Publisher's Version

[img] Text
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (511kB)
Item Type: Article
Type of entry: Secondary publication
Title: Investigation of Feature Engineering Methods for Domain-Knowledge-Assisted Bearing Fault Diagnosis
Language: English
Date: 24 November 2023
Place of Publication: Darmstadt
Year of primary publication: 2023
Place of primary publication: Basel
Publisher: MDPI
Journal or Publication Title: Entropy
Volume of the journal: 25
Issue Number: 9
Collation: 15 Seiten
DOI: 10.26083/tuprints-00024494
Corresponding Links:
Origin: Secondary publication DeepGreen

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.

Uncontrolled Keywords: bearing fault diagnosis, feature engineering, machine learning, condition monitoring, frequency band separation
Identification Number: 1278
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-244945
Additional Information:

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

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute for Product Development and Machine Elements (pmd)
Date Deposited: 24 Nov 2023 13:20
Last Modified: 04 Jan 2024 07:04
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/24494
PPN: 514429186
Actions (login required)
View Item View Item