Vibration analysis for early pitting detection during operation

Author:

Häderle Philipp,Merkle Lukas,Dazer Martin

Abstract

AbstractThe economic efficiency of machinery operation is significantly impacted by maintenance strategies. In the realm of condition-based or predictive maintenance strategies, the early detection of fatigue-induced damages is crucial. Therefore, this study focuses on the early detection of pitting damages during operation. Experimental investigations are conducted on a test gearbox acquiring acceleration data for different sizes of pitting damages under diverse operating conditions. A successful detection of the pitting damages during operation is achieved at a very early stage of progression, with a minimum size of 0.41% of an active tooth flank area. The utilization of design of experiments techniques facilitates the identification of factors influencing the detectability of pitting damages. The obtained results are analysed to elucidate the physical basis for the reliable detection of pitting damages across diverse operating conditions.

Funder

Universität Stuttgart

Publisher

Springer Science and Business Media LLC

Reference20 articles.

1. Randall RB (2011) Vibration-based condition monitoring: Industrial, aerospace and automotive applications. Wiley, Chichester

2. Gretzinger Y, Kroner A, Henß M, Dazer M, Bertsche B (2021) Extended Evaluation of Pitting Degradation Tests to Increase the Remaining Useful Life of Gear Wheels. Iop Conf Series: Mater Sci Eng 1097:

3. International Organization for Standardization: ISO 6336‑1 Calculation of load capacity of spur and helical gears—Part 1: Basic principles, introduction and general influence factors, 2019

4. International Organization for Standardization: ISO 6336‑2 Calculation of load capacity of spur and helical gears—Part 2: Calculation of surface durability (pitting), 2019

5. International Organization for Standardization: ISO 6336‑5 Calculation of load capacity of spur and helical gears—Part 5: Strength and quality of materials, 2016

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