Early detection of gear-tooth bending-fatigue damage by the Average-Log-Ratio ALR algorithm. (15th May 2022)

• Record Type: Journal Article
• Authors: Mark, William D.; Wagner, Matthew E.; Isaacson, Aaron C.
• Is Part Of: Mechanical systems and signal processing. Volume 171(2022)
• Publication Date: 2022-05-15
• Subjects: Tooth-bending-fatigue damage -- Gear damage detection -- Static transmission error; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.108922
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Gear-tooth bending-fatigue damage detection. Transfer-function representation of static-transmission-error. Static-transmission-error frequency-domain behavior. Abstract: This work illustrates and explains an application of the Average-Log-Ratio, ALR, gear-damage detection algorithm for a case of gear-tooth bending fatigue. Gear-vibration rotational-harmonic spans between adjacent tooth-meshing harmonics are combined in ALR, thereby greatly reducing the masking effects resulting from the meshing action of the mating gear with the damaged gear. The first observation of unambiguous tooth fracture is identified. Moreover, 100, 000 revolutions of the failing gear before this first observation of fracture, gear damage initiation, likely plastic deformation, is identified by ALR, thereby illustrating the potential for early warning of impending gear failure. Computation of kurtosis, FM4, using the same data, is provided for comparison of the detections provided by ALR and FM4, indicating earlier detection of damage initiation provided by ALR in comparison to that provided by FM4.