Helical gears are common in rotating machines, offering smoother operation and better durability compared to spur gears due to their continuous engagement and higher contact ratios. However, these advantages present significant challenges in fault detection, particularly in the early stages. Although advanced methods exist for fault detection, most are designed for spur gears, overlooking the complexities associated with helical gear dynamics. This research sheds new light on the complex physical phenomena associated with localized tooth faults in helical gears while proposing a sensitive fault detection strategy grounded in physical justification and extensive experimentation. The first study presents a new modeling approach for localized tooth breakage faults in helical gears, building on an existing validated dynamic model. This approach, validated experimentally, utilizes the multi-slice method to address the three-dimensional, time-variable contact line. For the first time, we refute the premise that fault severity in helical gears is necessarily correlated with the size of the damaged region. Instead, we provide a physically justified fault characterization through analysis of the gear mesh stiffness (GMS) and vibration signals. The second case study proposes a novel health indicator (HI) designed specifically for detecting localized tooth breakage faults in helical gears, relying on sensitive spectral analysis and energy-based feature extraction. This new HI is tested on vibration data measured through extensive controlled-degradation tests on helical gears, both in healthy conditions and with various severities of tooth breakage faults. The performance of the proposed HI is compared against traditional methods, showing significant superiority both in early and in more advanced stages of the fault. Our findings demonstrate the superiority of the new HI over traditional methods and provide crucial insights into the complex dynamic behavior of helical gears, offering practical value for improving reliability and safety in various engineering applications, such as in automotive, aerospace, and industrial machinery, and also paving the way for advanced fault severity estimation.
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Roee Cohen
Lior Bachar
Omri Matania
Structural Health Monitoring
Ben-Gurion University of the Negev
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Cohen et al. (Fri,) studied this question.
www.synapsesocial.com/papers/68d469c831b076d99fa66942 — DOI: https://doi.org/10.1177/14759217251369337
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