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Applied Sciences
Volume 15
Issue 22
10.3390/app152212346
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Case Study on Compression of Vibration Data for Distributed Wireless Condition Monitoring Systems

by
Rick Pandey
1,*,
Felix Grimm
2,3,
Dominik Nille
2,
Christoph Böckenhoff
2,
Jonathan Gamez
1,
Sebastian Uziel
1,
Albert Dorneich
2,
Tino Hutschenreuther
1 and
Silvia Krug
1,4
1
System Design Department, IMMS Institut für Mikroelektronik- und Mechatronik-Systeme Gemeinnützige GmbH (IMMS GmbH), 98693 Ilmenau, Germany
2
Balluff GmbH, 73765 Neuhausen auf den Fildern, Germany
3
Institute of Smart Sensors (IIS), University of Stuttgart, 70569 Stuttgart, Germany
4
Department of Computer and Electrical Engineering, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(22), 12346; https://doi.org/10.3390/app152212346 (registering DOI)
Submission received: 30 September 2025 / Revised: 30 October 2025 / Accepted: 17 November 2025 / Published: 20 November 2025
(This article belongs to the Special Issue Advances in Machinery Fault Diagnosis and Condition Monitoring)
Versions Notes

Abstract

To build robust condition monitoring solutions, it is important to identify signals that capture relevant information. However, how a degradation affects a given part of machinery might not be clear at the beginning. As a result, exploration measurement campaigns collecting large amounts of data are needed for initial evaluation. Vibration signals are typical examples of such data. Although, for explorative measurement campaigns, the battery-powered wireless node brings extra flexibility in terms of positioning the sensor at the desired location and facilitates retrofitting, the limited energy posed by them is the major downside. Sending high-sampled data over wireless channels is costly energy-wise if all samples are to be sent. When multiple sensor nodes transmit real-time measurement data concurrently over a wireless channel, the risk of channel saturation increases significantly. Avoiding this requires identifying an optimal balance between sampling time, transmission duration, and payload size. This can be done by processing and compressing data before transmission, on the sensor node close to the data acquisition and later reconstructing the received samples on the central node. In this paper, we analyze two compression mechanisms to ensure a good compression ratio and still allow good signal reconstruction for later analysis. We study two approaches, one based on the Fast Fourier Transform and one on Singular Value Decomposition, and discuss the pros and cons of each variant.
Keywords: Internet of Things; high-data-rate applications; compression; energy efficiency; condition monitoring; signal processing Internet of Things; high-data-rate applications; compression; energy efficiency; condition monitoring; signal processing

Share and Cite

MDPI and ACS Style

Pandey, R.; Grimm, F.; Nille, D.; Böckenhoff, C.; Gamez, J.; Uziel, S.; Dorneich, A.; Hutschenreuther, T.; Krug, S. Case Study on Compression of Vibration Data for Distributed Wireless Condition Monitoring Systems. Appl. Sci. 2025, 15, 12346. https://doi.org/10.3390/app152212346

AMA Style

Pandey R, Grimm F, Nille D, Böckenhoff C, Gamez J, Uziel S, Dorneich A, Hutschenreuther T, Krug S. Case Study on Compression of Vibration Data for Distributed Wireless Condition Monitoring Systems. Applied Sciences. 2025; 15(22):12346. https://doi.org/10.3390/app152212346

Chicago/Turabian Style

Pandey, Rick, Felix Grimm, Dominik Nille, Christoph Böckenhoff, Jonathan Gamez, Sebastian Uziel, Albert Dorneich, Tino Hutschenreuther, and Silvia Krug. 2025. "Case Study on Compression of Vibration Data for Distributed Wireless Condition Monitoring Systems" Applied Sciences 15, no. 22: 12346. https://doi.org/10.3390/app152212346

APA Style

Pandey, R., Grimm, F., Nille, D., Böckenhoff, C., Gamez, J., Uziel, S., Dorneich, A., Hutschenreuther, T., & Krug, S. (2025). Case Study on Compression of Vibration Data for Distributed Wireless Condition Monitoring Systems. Applied Sciences, 15(22), 12346. https://doi.org/10.3390/app152212346

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