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Vibration, Acoustics and Sensors Solutions for Machine Condition Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 3566

Special Issue Editor


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Guest Editor
Defence Science and Technology Group (DSTG), Fishermans Bend, Melbourne, VIC 3207, Australia
Interests: vibration analysis; machine condition monitoring; dynamic simulations; machine learning; predictive maintenance applications

Special Issue Information

Dear Colleagues,

The field of machine condition monitoring, using vibrations and acoustics signals, has progressed at a high rate, emerging from the mere use of traditional signal processing techniques to the application of advanced signal processing algorithms and the utilization of machine learning and artificial intelligence applications for incipient fault detection, diagnosis, and prognosis. This, along with the availability of high-tech sensors, high-power processing capability, and digital twins, has contributed extensively to the vital area of predictive maintenance (PM) and to the health and usage monitoring systems (HUMs) of assets.

This Special Issue aims to provide an opportunity to share some of your exciting high-quality research and innovative work on advances in machine condition monitoring to improve predictive maintenance and prognostics and health management in rotating machines.

Potential topics include, but are not limited to, the following:

  • Advanced signal processing techniques to extract fault features;
  • Signal processing and data fusion to detect, diagnose, and trend faults in rotating machines;
  • Sensor devices and sensing applications for machine condition monitoring;
  • Machine learning and artificial intelligence (AI) applications applied to vibrations and acoustics signals for predictive maintenance and prognostics and health management;
  • Dynamic simulations and virtual twins for the better understanding of rotating machines and the development of health and usage monitoring systems (HUMs).

Dr. Nader Sawalhi
Guest Editor

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Keywords

  • vibration
  • acoustics
  • rotating machines
  • predictive maintenance (PM)
  • health and usage monitoring systems (HUMSs)
  • machine learning
  • digital twin
  • diagnosis
  • prognosis
  • prognostics and health management (PHM)
  • artificial intelligence (AI)
  • incipient fault detection
  • data fusion

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Published Papers (4 papers)

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Research

29 pages, 12505 KiB  
Article
Improved Order Tracking in Vibration Data Utilizing Variable Frequency Drive Signature
by Nader Sawalhi
Sensors 2025, 25(3), 815; https://doi.org/10.3390/s25030815 - 29 Jan 2025
Abstract
Variable frequency drives (VFDs) are widely used in industry as an efficient means to control the rotational speed of AC motors by varying the supply frequency to the motor. VFD signatures can be detected in vibration signals in the form of sidebands (modulations) [...] Read more.
Variable frequency drives (VFDs) are widely used in industry as an efficient means to control the rotational speed of AC motors by varying the supply frequency to the motor. VFD signatures can be detected in vibration signals in the form of sidebands (modulations) induced on tonal components (carrier frequencies). These sidebands are spaced at twice the “pseudo line” VFD frequency, as the magnetic forces in the motor have two peaks per current cycle. VFD-related signatures are generally less susceptible to interference from other mechanical sources, making them particularly useful for deriving speed variation information and obtaining a “pseudo” tachometer from the motor’s synchronous speed. This tachometer can then be employed to accurately estimate the speed profile and to facilitate order tracking in mechanical systems for vibration analysis purposes. This paper presents a signal processing technique designed to extract a pseudo tachometer from the VFD signature found in a vibration signal. The algorithm was tested on publicly available vibration data from a test rig featuring a two-stage gearbox with seeded bearing faults operating under variable-speed conditions with no load, i.e., with minimal slip between the induction motor’s synchronous and actual speed. The results clearly demonstrate the feasibility of using VFD signatures both to extract an accurate speed profile (root mean square error, RMSE of less than 2.5%) and to effectively perform order tracking, leading to the identification of bearing faults. This approach offers an accurate and reliable tool for the analysis of vibration in mechanical systems driven by AC motors with VFDs. However, it is important to note that some inaccuracies may occur at higher motor slip levels under heavy or variable loads due to the mismatch between the synchronous and actual speeds. Slip-induced variations can further distort tracked order frequencies, compromising the accuracy of vibration analysis for gear mesh and bearing defects. These issues will need to be addressed in future research. Full article
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19 pages, 3105 KiB  
Article
Investigating the Effect of Vibration Signal Length on Bearing Fault Classification Using Wavelet Scattering Transform
by Suparerk Janjarasjitt
Sensors 2025, 25(3), 699; https://doi.org/10.3390/s25030699 - 24 Jan 2025
Viewed by 240
Abstract
Bearing condition monitoring and prognosis are crucial tasks for ensuring the proper operation of rotating machinery and mechanical systems. Vibration signal analysis is one of the most effective techniques for bearing condition monitoring and prognosis. In this study, the wavelet scattering transform, derived [...] Read more.
Bearing condition monitoring and prognosis are crucial tasks for ensuring the proper operation of rotating machinery and mechanical systems. Vibration signal analysis is one of the most effective techniques for bearing condition monitoring and prognosis. In this study, the wavelet scattering transform, derived from wavelet transforms and incorporating concepts from scattering transform and convolutional network architectures, was utilized to extract quantitative features from vibration signals. The number of wavelet scattering coefficients obtained from vibration signals of different lengths varied due to the use of predefined wavelet and scaling filters in the wavelet scattering network. Additionally, these wavelet scattering coefficients are associated with different scattering paths within the corresponding wavelet scattering networks. Eight different lengths of vibration signals, associated with fifteen classes of rolling element bearing faults and conditions, were investigated using wavelet scattering feature extraction. The classes of rolling element bearing faults and conditions included normal bearings as well as ball and inner race faults with various fault diameters ranging from 0.007 inches to 0.028 inches. For the specific dataset validated, the computational results indicated that excellent bearing fault classification was achievable using wavelet scattering feature vectors derived from vibration signals with lengths of at least 6000 samples. Full article
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32 pages, 6746 KiB  
Article
Determination of Vibration Properties and Reliable Frequency Estimation for Synchronous Vibrations Through Improved Blade Tip Timing Techniques Without a Once-per-Revolution Sensor
by Marios Sasakaros, Luca Mann, Markus Schafferus and Manfred Wirsum
Sensors 2025, 25(2), 489; https://doi.org/10.3390/s25020489 - 16 Jan 2025
Viewed by 1014
Abstract
Synchronous vibrations, which are caused by periodic excitations, can have a severe impact on the service life of impellers. Blade Tip Timing (BTT) is a promising technique for monitoring synchronous vibrations due to its non-intrusive nature and ability to monitor all blades at [...] Read more.
Synchronous vibrations, which are caused by periodic excitations, can have a severe impact on the service life of impellers. Blade Tip Timing (BTT) is a promising technique for monitoring synchronous vibrations due to its non-intrusive nature and ability to monitor all blades at once. BTT generally employs a Once-per-Revolution (OPR) sensor that is mounted on the shaft for blade identification and deflection calculation. Nevertheless, OPR sensors can be unreliable, as they may be affected by shaft vibrations, and their implementation can be restricted by space constraints. Moreover, the low number of BTT sensors typically leads to under-sampled deflection signals, which consequently hinders the estimation of the vibration frequencies due to aliasing problems. For this reason, BTT is commonly accompanied by strain gauge (SG) measurements on some blades. In this paper, improved BTT techniques are presented, which enable the determination of vibration properties of synchronous vibrations without the need for an OPR sensor and ensure a reliable frequency assessment. Specifically, the blades are identified by unique characteristics resulting from manufacturing tolerances, while the blade deflections are calculated through a novel method, which relies on the impeller’s circumferential position. The proposed method enables accurate OPR-free calculation of blade deflections, by accounting for speed variations within a revolution and considering the actual blade positions on the impeller. By completely eliminating the need for an OPR sensor, the accuracy of BTT is enhanced, as the blade deflections are no longer affected by shaft vibrations, while speed variations within a revolution can be accounted for. Moreover, the implementation possibilities of BTT are improved, allowing its application in systems, where an OPR sensor cannot be instrumented due to space constraints. Subsequently, the vibration frequencies are accurately estimated, by employing an improved Multi-Sampling method based on Non-Uniform Fast Fourier Transform. This approach enables the blind analysis of BTT measurements and can identify multiple vibration frequencies. The proposed method expands the capabilities of BTT through a reliable assessment of vibration frequencies from under-sampled BTT signals. Therefore, it is no longer necessary to accompany BTT measurements with SG measurements for frequency identification. Finally, the vibration properties are determined using regression models. The proposed BTT techniques are validated through comparison with SG measurements as well as a commercial BTT system, using experimental data from a test bench of a turbocharger used for marine applications. The vibrations were recorded under real operating conditions, thus demonstrating the industrial applicability of the proposed BTT evaluation procedure. Full article
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20 pages, 1237 KiB  
Article
Recursive Engine In-Cylinder Pressure Reconstruction Using Sensor-Fused Engine Speed
by Runzhe Han, Christian Bohn and Georg Bauer
Sensors 2024, 24(16), 5237; https://doi.org/10.3390/s24165237 - 13 Aug 2024
Viewed by 969
Abstract
The engine in-cylinder pressure is a very important parameter for the optimization of internal combustion engines. This paper proposes an alternative recursive Kalman filter-based engine cylinder pressure reconstruction approach using sensor-fused engine speed. In the proposed approach, the fused engine speed is first [...] Read more.
The engine in-cylinder pressure is a very important parameter for the optimization of internal combustion engines. This paper proposes an alternative recursive Kalman filter-based engine cylinder pressure reconstruction approach using sensor-fused engine speed. In the proposed approach, the fused engine speed is first obtained using the centralized sensor fusion technique, which synthesizes the information from the engine vibration sensor and engine flywheel angular speed sensor. Afterwards, with the fused speed, the engine cylinder pressure signal can be reconstructed by inverse filtering of the engine structural vibration signal. The cylinder pressure reconstruction results of the proposed approach are validated by two combustion indicators, which are pressure peak Pmax and peak location Ploc. Meanwhile, the reconstruction results are compared with the results obtained by the cylinder pressure reconstruction approach using the calculated engine speed. The results of sensor fusion can indicate that the fused speed is smoother when the vibration signal is trusted more. Furthermore, the cylinder pressure reconstruction results can display the relationship between the sensor-fused speed and the cylinder pressure reconstruction accuracy, and with more belief in the vibration signal, the reconstructed results will become better. Full article
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