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Applied Sciences
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Novel Approaches for Fault Diagnostics of Machine Elements
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Novel Approaches for Fault Diagnostics of Machine Elements

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 3838

Special Issue Editors

Prof. Dr. Eckhard Kirchner

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Guest Editor
Institute for Product Development and Machine Elements, Technical University of Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany
Interests: electric properties of tribosystems; sensing machine elements; sensor integration; design for additive manufacturing; potential of additive technologies; respective development methods
Prof. Dr. Tobias Melz

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Guest Editor
Fraunhofer Institute for Structural Durability and System Reliability LBF, Director, Bartningstr. 47, 64289 Darmstadt, Germany
Interests: lightweight design; smart structures; vibration and noise control; reliability engineering and SHM
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Uwe Klingauf

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Guest Editor
Institute of Flight Systems and Automatic Control, Technical University of Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany
Interests: health and usage monitoring; prognostics and health management; predictive maintenance; reliability engineering
Dr. Sven Herold

E-Mail Website
Guest Editor
Fraunhofer Institute for Structural Durability and System Reliability LBF, Division director Smart Structures, Bartningstr. 47, 64289 Darmstadt, Germany
Interests: lightweight design; smart structures; active and passive vibration technologies; design methodologies
Special Issues, Collections and Topics in MDPI journals
Florian Becker-Dombrowsky

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Guest Editor Assistant
Institute for Product Development and Machine Elements, Technical University of Darmstadt, Otto-Berndt-Straße 2, 64287 Darmstadt, Germany
Interests: machine elements; smart machine elements; rolling bearings; transmissions; condition monitoring; RUL prediction; predictive maintenance

Special Issue Information

Dear Colleagues,

Faults of machine elements are considered a major root cause in all technical equipment, causing considerable economic damage. The reduction of unexpected and unnecessary maintenance intervals is one of the major opportunities to improve productivity or profitability. Hence, extensive research has been decidated to the development of new methods to detect faults earlier, preferably before the consecutive breakdown of technical systems. However, in most cases, fault diagnosis of machine elements (the backbone of technical systems) is restricted to conventional vibration analysis in lower frequency bands, which often requires a significant fault to detect changes in the characteristic sound and vibration finger print of a machine.

The target of this Special Issue is to compile a comprehensive overview of contemporary and novel approaches to fault diagnostic of machine elements. The focus is on non-destructive measuring, evaluation, and signal processing techniques. In addition, the approaches must, at least, be verified in typical machine element applications. The algorithms need to be validated in experiments to provide results superior to the current state of research.

Prof. Dr. Eckhard Kirchner
Prof. Dr. Tobias Melz
Prof. Dr. Uwe Klingauf
Dr. Sven Herold
Guest Editors

Florian Michael Becker-Dombrowsky
Guest Editor Assistant

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Keywords

  • fault diagnosis
  • impedance measurement
  • predictive maintenance
  • predictive health monitoring
  • non-destructive measurement
  • feature engineering
  • rolling element bearings
  • plain and journal bearings
  • involute gears
  • bevel and worm gear drives
  • shiftable and non shiftable clutches
  • shaft–hub connections
  • sealing components

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Published Papers (3 papers)

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Research

21 pages, 4767 KiB  
Article
Optimizing Bearing Fault Diagnosis in Rotating Electrical Machines Using Deep Learning and Frequency Domain Features
by Eduardo Quiles-Cucarella, Alejandro García-Bádenas, Ignacio Agustí-Mercader and Guillermo Escrivá-Escrivá
Appl. Sci. 2025, 15(6), 3132; https://doi.org/10.3390/app15063132 - 13 Mar 2025
Viewed by 513
Abstract
This study uses deep learning techniques to optimize fault diagnosis in rolling element bearings of rotating electrical machines. Leveraging the Case Western Reserve University bearing fault database, the methodology involves transforming one-dimensional vibration signals into two-dimensional scalograms, which are used to train neural [...] Read more.
This study uses deep learning techniques to optimize fault diagnosis in rolling element bearings of rotating electrical machines. Leveraging the Case Western Reserve University bearing fault database, the methodology involves transforming one-dimensional vibration signals into two-dimensional scalograms, which are used to train neural networks via transfer learning. By employing SqueezeNet—a pre-trained convolutional neural network—and optimizing hyperparameters, this study significantly reduces the computational resources and time needed for effective fault classification. The analysis evaluates the effectiveness of two wavelet transforms (amor and morse) for feature extraction in correlation with varying learning rates. Results indicate that precise hyperparameter tuning enhances diagnostic accuracy, achieving a classification accuracy of 99.37% using the amor wavelet. Scalograms proved particularly effective in identifying distinct vibration patterns for faults in bearings’ inner and outer races. This research underscores the critical role of advanced signal processing and machine learning in predictive maintenance. The proposed methodology ensures higher reliability and operational efficiency and demonstrates the feasibility of transfer learning in industrial diagnostic applications, particularly for optimizing resource-constrained systems. These findings improve the robustness and precision of machine fault diagnosis systems. Full article
(This article belongs to the Special Issue Novel Approaches for Fault Diagnostics of Machine Elements)
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19 pages, 11430 KiB  
Article
Simulative and Experimental Investigation of Vibration Transfer Path at Gearboxes
by Erich Knoll, Chaokai Chen, Michael Otto and Karsten Stahl
Appl. Sci. 2025, 15(6), 3109; https://doi.org/10.3390/app15063109 - 13 Mar 2025
Viewed by 360
Abstract
Condition monitoring systems are widely used in gearboxes. Gears are one of the most crucial components for power transmission. Hence, the optimal sensor positions for condition monitoring of gears should be investigated to maximize reliability and to minimize costs. This work aims to [...] Read more.
Condition monitoring systems are widely used in gearboxes. Gears are one of the most crucial components for power transmission. Hence, the optimal sensor positions for condition monitoring of gears should be investigated to maximize reliability and to minimize costs. This work aims to analyze measured signals from rotating sensors at gears and compare them to signals from housing sensors to find the suitable positions for condition monitoring of the gears. Additionally, the rotational speed and external torque influences on the signal quality have been investigated. These are compared with a simulation model, which considers the vibration excitation from the gear mesh and bearings. The results show that the rotational speed affects the amplitude of the excitation. On this basis, we also investigate the amplitudes of the excitation frequencies of interest. The ratio of the amplitudes of these frequencies related to the mean values of the measurement signals is called the peak-to-mean ratio (PMR), and this PMR corresponds to the speed which is of interest for automatic fault detection in the gearboxes. Additionally, the simulation results show that the intensity of the vibration with the gear mesh frequency hardly reduces during transmission through the tapered roller bearings. Full article
(This article belongs to the Special Issue Novel Approaches for Fault Diagnostics of Machine Elements)
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17 pages, 4769 KiB  
Article
Evaluation of Frequency Effects on Fatigue Life at High Test Frequencies for SAE 1045 Steel Based on Thermography and Electrical Resistance Measurements
by Jonas Anton Ziman, Fabian Weber, Janina Koziol, Johannes Leon Otto, Lukas Maximilian Sauer, Frank Walther and Peter Starke
Appl. Sci. 2025, 15(3), 1022; https://doi.org/10.3390/app15031022 - 21 Jan 2025
Viewed by 2319
Abstract
This research provides a method for a reliable fatigue life estimation at high testing frequencies. The investigations are based on the lifetime prediction method StressLifeHCF considering test frequencies of 80 and 260 Hz for normalized SAE 1045 (C45E, 1.1191) steel. Therefore, load [...] Read more.
This research provides a method for a reliable fatigue life estimation at high testing frequencies. The investigations are based on the lifetime prediction method StressLifeHCF considering test frequencies of 80 and 260 Hz for normalized SAE 1045 (C45E, 1.1191) steel. Therefore, load increase tests and constant amplitude tests were carried out using a resonant testing rig. To ensure a mechanism-oriented lifetime prediction, the material response to dynamic loading is monitored via temperature and electrical resistance measurements. Due to the higher energy input per time unit, when the test frequency is increased, the heat dissipation also increases. For this reason, a precise differentiation between frequency- and temperature-related effects for adequate fatigue assessment is challenging. To evaluate the temperature’s influence on electrical resistance, an electrical resistance-temperature hysteresis is measured, and the frequency influence is analyzed by considering cyclic deformation curves. In addition to an extension of the fatigue life due to an increased test frequency, the lifetime prediction method was validated for high frequencies. The generated S-N curves show a reliable agreement with the data points from conventional constant amplitude tests. In this context, the temperature correction of the electrical resistance proved to be an important input variable for a reliable lifetime prediction. Full article
(This article belongs to the Special Issue Novel Approaches for Fault Diagnostics of Machine Elements)
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