Strain-based condition monitoring of inner raceways of deep-groove ball bearings using FBG sensors
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https://orcid.org/0000-0001-7371-626X
Damilare Samuel Ojo
Abhinit Hirde
Ted Ooijevaar
Francis Berghmans
Sidney Goossens
Abstract
Rolling element bearings are critical components of rotating machinery, whose failure is one of the main causes of downtime and maintenance. Traditional methods of condition monitoring of bearings, based on accelerometers or acoustic emission sensors and vibration analysis, are prone to signal attenuation and interference in the transfer path due to other machine components. As an alternative, fiber Bragg grating (FBG) sensors allow for quasi-distributed sensing of the local strain of the bearing, as they can be integrated in a single optical fiber bonded directly onto a bearing raceway. They offer several advantages, such as compactness, immunity to electromagnetic interference (EMI), and resistance to corrosion. Proximity and the quasi-distributed nature of FBG-based strain sensing are key properties to obtain significantly higher signal-to-noise ratio (SNR) and sensitivity, enabling enhanced fault diagnosis and localization.
In this work, we analyze the strain signals obtained during an accelerated lifetime test (ALT) of a deep-groove ball bearing. The FBGs are instrumented on the rotating inner raceway of the bearing, going beyond the implementation on the static outer raceway performed by several recent research works. We study the behaviour of the FBG signals and their features during the complete evolution of a surface-initiated fatigue fault on the inner ring, and evaluate their capabilities for simultaneous fault detection and localization. We observe that two features are reliable indicators for fault detection and localization, the RMS of the high-pass filtered FBG signals, and the sum of values of their squared envelope spectrum (SES) at the harmonics of the ball pass frequency on the inner race (BPFI), while the peak-to-peak (P2P) value is not.
How to Cite
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Optical Sensing, Fiber Bragg Grating, Rotating Machinery, Bearing Diagnosis, Strain Sensing, Fault Localization, Fault Detection
Eryilmaz, K., de la Hucha Arce, F., Zegers, J., & Ooijevaar, T. (2024). Noise-aware AI methods for robust acoustic monitoring of bearings in industrial machines. PHM Society European Conference, 8. doi: 10.36001/phme.2024.v8i1.4112
Goossens, S., Berghmans, F., Muñoz, K., Jiménez, M., Karachalios, E., Saenz-Castillo, D., & Geernaert, T. (2021). A global assessment of barely visible impact damage for CFRP sub-components with FBG-based sensors. Composite Structures, 272, 114025. doi: https://doi.org/10.1016/j.compstruct.2021.114025
Goossens, S., Mantas, P., Ojo, D., Axinte, O., Mauricio, A., Gryllias, K., & Berghmans, F. (2025). Repeatability assessment of FBG-based strain measurements for bearing fault diagnostics. Journal of Physics: Photonics, 7(3), 1–13. doi: 10.1088/2515-7647/adec29
Halme, J., & Andersson, P. (2009). Rolling contact fatigue and wear fundamentals for rolling bearing diagnostics: State of the art. Journal of Engineering Tribology, 224, 377–393.
Hill, K., & Meltz, G. (1997). Fiber Bragg grating technology fundamentals and overview. Journal of Lightwave Technology, 15(8), 1263–1276. doi: 10.1109/50.618320
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510. doi: https://doi.org/10.1016/j.ymssp.2005.09.012
Maljaars, E., Ravesloot, J., Janssen, A., Mol, H., Murguia, C., & Fey, R. (2025). On the potential of rolling bearing strain signals for spall size estimation: Modeling and experiments. Mechanical Systems and Signal Processing, 240, 113384. doi: https://doi.org/10.1016/j.ymssp.2025.113384
Medvedovsky, D., Ohana, R., Klein, R., Tur, M., & Bortman, J. (2022). Spall length estimation based on strain model and experimental FBG data. Mechanical Systems and Signal Processing, 171, 108923. doi: https://doi.org/10.1016/j.ymssp.2022.108923
Ooijevaar, T., Pichler, K., Di, Y., Devos, S., Volckaert, B., Hoecke, S. V., & Hesch, C. (2019). Smart machine maintenance enabled by a condition monitoring living lab. IFAC-PapersOnLine, 52(15), 376–381.
Quatrini, E., Costantino, F., Di Gravio, G., & Patriarca, R. (2020). Condition-based maintenance: An extensive literature review. Machines, 8(2). doi: 10.3390/machines8020031
Randall, R. B. (2011). Vibration-based condition monitoring: Industrial, aerospace and automotive applications. John Wiley & Sons.
Randall, R. B., & Antoni, J. (2011). Rolling element bearing diagnostics: A tutorial. Mechanical Systems and Signal Processing, 25(2), 485–520.
Zhang, H., Borghesani, P., Randall, R. B., & Peng, Z. (2022). A benchmark of measurement approaches to track the natural evolution of spall severity in rolling element bearings. Mechanical Systems and Signal Processing, 166, 108466. doi: https://doi.org/10.1016/j.ymssp.2021.108466
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