The Frequency of the Blue Signal Is 10hz The Signal Is Sampled Using a Sampling Frequency of 8hz

Abstract

Structural health monitoring (SHM) is imperative to the safety of structures, but can be costly and limited to a few locations for coverage. Many advancements have been made in the field of SHM over the past years such as the use of wireless sensors, implementation of compressed sensing, and event-based monitoring. These advancements are all pursued with the combined goal of collecting relevant data from the structure in a cost-effective manner, as well as taking into account the limitations placed on the system including size, energy consumption, safety, and bandwidth. Developments in wireless sensor systems enable greater coverage of structure monitoring since wired systems are costly to install. For wireless systems, it is ideal to use as little power as possible to process signals to reduce the sensor battery use and costly battery replacement. Previous research has focused on compressive sensing to reduce the size of data transferred while maintaining high-fidelity signal analysis. More recent work has focused on event-based monitoring, which collects data based on non-uniformly spaced triggers. Both compressive sensing techniques and event-based monitoring focus on improving sampling for multi-sensor systems, but are restricted to lower sampling rates or are prone to missed events. In order to further advance the application of compressive sensing techniques and event-based monitoring, this project will focus on triggering schemes for multi-sensor systems with signals above the Nyquist frequency of the individual sensors, assuming the signal is present across all sensor recordings. The problem with detecting frequencies above the Nyquist frequency is that the signals will alias and show up as a disguised frequency. Using existing sensors to determine the true frequencies of the signal requires a new method of detection. Multiple sensors were placed around a structure of interest and the optimal time delay of each sensor was determined through simulation and affirmed by experimentation.

Keywords

• SHM
• Compressive sensing
• Nyquist frequency
• Under-sampling

References

1. Lynch, J., Law, K., Kiremidjian, A., Kenny, T., Carryer, E.: A wireless modular monitoring system for civil structures, In: Proceedings of the 20th International Modal Analysis Conference (IMAC XX), Los Angeles, CA, 01 (2002)

   Google Scholar

2. Candes, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)

   CrossRef  Google Scholar

3. Mascareñas, D., Cattaneo, A., Theiler, J., Farrar, C.: Compressed sensing techniques for detecting damage in structures. Struct. Health Monit. 12(4), 325–338 (2013)

   CrossRef  Google Scholar

4. Silva, M., Figueiredo, E., Costa, J., Mascarenas, D.: Event-Based Phased Array Signal Processing, 11 (2019)

   Google Scholar

5. Dasari, S., Dorn, C., Yang, Y., Larson, A., Mascareñas, D.: A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions. J. Intell. Mater. Syst. Struct. 29(17), 3456–3481 (2018)

   CrossRef  Google Scholar

6. Hall, D., Llinas, J.: An introduction to multisensor data fusion. Proc. IEEE 85(1), 6–23 (1997)

   CrossRef  Google Scholar

7. Colford, G., Jacobson, E., Plewe, K., Flynn, E., Wachtor, A.: Remote Detection of Abnormal Behavior in Mechanical Systems. In: Niezrecki, C., Baqersad, J., Di Maio, D. (eds.) Rotating Machinery, Optical Methods & Scanning LDV Methods, Vol. 6, pp. 59–69. Springer International Publishing, Cham (2019)

   Google Scholar

8. Sohn, H., Farrar, C.R., Hemez, F.M., Czarnecki, J.J.: A Review of Structural Health Review of Structural Health Monitoring Literature 1996-2001. 1 (2002)

   Google Scholar

9. Achenbach, J.D.: Structural health monitoring – what is the prescription?. Mech. Res. Commun. 36(2), 137–142 (2009)

   CrossRef  Google Scholar

10. Mishra, C., Samantaray, A., Chakraborty, G.: Ball bearing defect models: a study of simulated and experimental fault signatures. J. Sound Vibr. 400, 86–112 (2017)

    CrossRef  Google Scholar

Download references

Author information

Authors and Affiliations

1. Dept. of Mechanical Eng., University of Utah, Salt Lake City, UT, USA

   Sean Detwiler

2. Dept. of Mechanical Eng., Georgia Institute of Technology, Atlanta, GA, USA

   Erik Barbosa

3. Dept. of Mechanical Eng. and Dept. of Mathematics, Lafayette College, Easton, PA, USA

   Kristen Steudel

4. Los Alamos National Laboratory, Los Alamos, NM, USA

   Jeffery Tippmann, Christian Ward & Brian West

Corresponding author

Correspondence to Jeffery Tippmann .

Editor information

Editors and Affiliations

1. Engineering Technology, University of Twente, ENSCHEDE, Overijssel, The Netherlands

   Ph.D. Dario Di Maio

2. Kettering University, Flint, MI, USA

   Assist. Prof. Javad Baqersad

Rights and permissions

Copyright information

© 2023 The Society for Experimental Mechanics, Inc.

About this paper

Cite this paper

Detwiler, S., Barbosa, E., Steudel, K., Tippmann, J., Ward, C., West, B. (2023). Multi-Sensor Collaborative Sampling Schemes to Reconstruct Undersampled Mechanical System Signals for Machinery Fault Detection. In: Di Maio, D., Baqersad, J. (eds) Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-04098-6_6

Download citation

• .RIS
• .ENW
• .BIB

• DOI : https://doi.org/10.1007/978-3-031-04098-6_6

• Published: 31 March 2022

• Publisher Name: Springer, Cham

• Print ISBN: 978-3-031-04097-9

• Online ISBN: 978-3-031-04098-6

• eBook Packages: Engineering Engineering (R0)

figueroanold1990.blogspot.com

Source: https://link.springer.com/chapter/10.1007/978-3-031-04098-6_6

Share this post