ABSTRACT
Acoustic Emission (AE) has seen increased popularity in applications involving machine condition monitoring. AE applications usually involve higher sampling rate than vibration signals, not rare reaching 2 MHz. One of the main challenges involving AE based fault diagnosis is the need of preprocessing massive amounts of data generated by this technique, including engineering of appropriate features and dimensionality reduction so to be able to handle such massive datasets. In this paper, we propose a novel method based on Deep Convolutional Neural Networks (CNN) to handle raw AE signals for diagnosis of a system’s health states. This method is flexible enough to not only handle the massive amount of AE data, but also to provide the means for automatic feature extraction by applying various filters to the raw AE signals, and thus identifying relevant frequencies related to different faults. The proposed CNN method is applied to fatigue crack detection on blades of an experimental rotor.
