Use of an improved vibration-based updating methodology for gear wear prediction. (February 2021)

• Record Type: Journal Article
• Authors: Feng, Ke; Smith, Wade A.; Peng, Zhongxiao
• Is Part Of: Engineering failure analysis. Volume 120(2021)
• Publication Date: 2021-02
• Subjects: Gear wear prediction -- Vibration -- Dynamic model -- Tribological model -- Wear depth; 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.2020.105066
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: Gear wear can be monitored and predicted under different lubrication conditions. A new approach is proposed to calculate the wear depth distribution of gears. The worn gear profile obtained from the improved model is more reasonable and realistic. Two endurance tests under different lubrication conditions are applied to show the effectiveness and efficiency of the proposed methodology. Abstract: Having the ability to predict the gear surface wear progression can bring significant benefits in safety and cost to various industries. Gear wear can result in a change in the working surface geometry of the gear tooth, which affects the tooth surface load distribution. The altered load distribution in turn promotes the progression of gear wear. The coupling effect between gear wear and gear dynamics results in complex vibration features, making it difficult to extract wear-related vibration features and develop specific vibration-based gear wear prediction techniques. It was recently proposed that the combination of a dynamic model, a tribological model and the updating of vibration feature(s) can be used for wear prediction. In that vibration-based updating methodology, the dynamic model is used to quantitatively study wear-induced vibrations. The outputs of the dynamic model, the contact forces, are the inputs to the tribological model, which calculates the wear depth distribution. Then the gear tooth profile is updated in the dynamic model by inputting a new geometric transmission error (GTE), which stands for the worn gear profile. New contact force and vibration signals are then acquired from the dynamic model, and then this process is repeated to produce estimated gear wear profiles, approximating some future state of the worn gear. To improve the accuracy of gear wear prediction using the vibration-based updating methodology, in this paper, the tribological model is improved with consideration of contact pressure. Moreover, a new approach is proposed to calculate the wear depth, with consideration of the effect of Hertzian deformation, giving a contact area rather than a line. The effectiveness of the improved updating methodology in gear wear prediction is evaluated using two endurance tests under different lubrication conditions. The estimated wear depths are compared to the measured ones to assess the performance of the updating approach. This study demonstrates that the improved vibration-based updating methodology has the ability to track and correct for changes in the gear wear rates, thus allowing reliable gear wear prediction.