Abstract

Purpose: This literature review aims to assess the current state of research concerning the use of acoustic emission (AE) analysis for gear condition monitoring and fault detection. There is a particular focus on wind turbine gearboxes and modeling of AE generated by gear transmissions. It identifies key challenges and opportunities for advancing AE analysis as an effective alternative to traditional vibration analysis. Methods: The review critically examines a range of experimental studies and application cases from the last decades, including those utilizing different approaches such as envelope spectrum analysis, wavelet transform, empirical mode decomposition, and machine learning. It also analyzes models developed for predicting AE based on the interaction of two simple surfaces in sliding contact. Results: Findings reveal that AE analysis has seen significant advancements but is largely restricted by its experimental nature. Although several advanced signal processing techniques have been applied, a standard procedure for AE analysis in gear transmissions is yet to be established. Moreover, existing models for predicting AE often overlook factors such as lubrication and surface roughness, affecting their applicability. Conclusion: The development of an analytical model that predicts AE signatures based on specific gear transmission parameters and potential faults is crucial. This need sets AE apart from vibration analysis, which already boasts numerous dynamic, geometric, and phenomenological models. Addressing this gap is essential to progress AE analysis as a reliable process for condition monitoring and fault detection in gear transmissions.