Vibration-based gear condition monitoring using an improved section-specific approach without the need of historic reference data

Different kinds of damage can compromise the operation of a gearbox, which might lead to unexpected downtime and maintenance. Thus, there are various approaches in the field of vibration based gear condition monitoring, which aim to detect and distinguish the kind and the intensity of gear damage during run-time. With many of the approaches relying on the comparison with past data, it remains challenging to detect damage without knowledge of historic references. Therefore, we developed an improved, gear geometry related and tooth-specific condition monitoring approach, which allows the comparison of gear sectional features to each other and does not need prior knowledge of the gears' angular position. The proposed method provides a damage criterion and aims to detect local damage such as pitting, which primarily occur on one or few flanks, thereby taking advantage of different signal features resulting from damaged und undamaged gear tooth flanks. Using order-tracked signals enables angular, tooth number related section-to-section comparisons. This allows irregularities in specific geometric gear parts being identified as compared to other parts. Additionally, the proposed condition monitoring method includes an optimization of the angular section distribution, which maximizes the damage criterion's sensitivity and makes prior knowledge of the gear's angular positions obsolete. To validate the proposed method, we used a single-staged, helical gear experiment with occurring pitting damage. The section-specific damage criterion results in a significant increase due to pitting damage. Hence, the proposed section-specific approach provides improved damage detection capabilities without historic reference data or angular gear position knowledge.