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Application of Acoustic Emission Technique for Online Evaluation and Classification of Wear State



This paper introduces a low cost system for online detection, classification, and examination of wear phenomena in metallic structure based on the application of the acoustic emission (AE) technique and advanced signal processing approaches. First applications of suitable filtering techniques to define relevant data are explained. The main idea introduced here is to define and to monitor specific AE properties of a tribological system. Therefore experimental results, based on a test-rig for wear examination with variable lubrication and normal force are used. The measured AE signals are analyzed mainly by time-frequency analysis utilizing Xilinx-based FPGA board. The key idea is to connect signal features unambiguously to their unique sources, e.g. adhesive and abrasive wear, surface fatigue and so on to realize diagnosis (judging the system state) and prognosis (calculating State-of-Damage), and to monitor and control the effects. In addition to the method and filtering related aspects the measurements as well as the filtering hardware are explained and detailed results distinguishing different wear-related effects during different stages of the system’s life are given.

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