Impact of gear tooth surface quality on detection of local faults. (January 2020)

• Record Type: Journal Article
• Authors: Dadon, I.; Koren, N.; Klein, R.; Lipsett, M.G.; Bortman, J.
• Is Part Of: Engineering failure analysis. Volume 108(2020)
• Publication Date: 2020-01
• Subjects: Gear diagnostics -- Profile deviations -- Dynamic model -- Condition monitoring -- Vibration; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2019.104291
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: Effects of gear tooth surface quality on vibration-based diagnostic capability. A modified analytical expression for describing the tooth surface. Using a dynamic model for simulations of gear vibrations. Validating the model simulations with experiments under similar conditions. Abstract: A practical gear transmission contains surface imperfections that directly influence the vibration signature. The surface imperfections may restrict the ability to diagnose faults using vibration analysis. This study uses numerical simulation of the gear tooth surface interactions to better understand the limits of fault detection in the presence of different levels of gear imperfections. The tooth profile deviation is defined with respect to the involute profile. A dynamic model was used to simulate the vibrations of gears with different types and sizes of faults. The model takes into consideration the irregularity of the gear teeth representing a realistic system. For the purpose of verification, simulations were compared with data from a series of experiments under similar conditions. It is shown that low-precision gear profile grade reduces the ability to detect faults; however, gears with high-quality tooth surfaces allow detection of smaller faults (1.5 mm) and classification of the fault size. It is demonstrated that the model predicts correctly the detectable fault severity for a given profile precision grade. Moreover, the model estimates the distribution of results generated by profile deviations, allowing robust analysis of diagnostic capabilities.