Planetary gearboxes (PGBs) are one of the most critical components in the drivetrain of many industrial and military equipment systems such as wind turbines and helicopters. Therefore, developing effective PGB fault diagnostic methods is important. The industries currently utilize vibratory analysis as a standard method for PGB condition monitoring and fault diagnosis. However, acoustic emission (AE) techniques represent a more attractive alternative to PGB fault diagnosis as AE sensors can potentially be more sensitive to the incipient faults than vibration sensors. A key to the success of PGB fault diagnosis using AE signals is the effective processing of AE signals and extracting useful features from the AE signals. In this paper, a natural language processing (NLP) based deep learning architecture, the transformer architecture is applied for PGB fault diagnosis with AE signals. The transformer architecture uses a multi-head attention mechanism that enables attending to different fault features in the AE signals. The effectiveness of the presented approach is validated on a set of seeded localized faults on all gears in a laboratory PGB: sun gear, planetary gear, and ring gear.
A Natural Language Processing Based Planetary Gearbox Fault Diagnosis with Acoustic Emission Signals
2023 IEEE Aerospace Conference ; 01-06
04.03.2023
806334 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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