Detection of modulated chatter using moving average difference spectrum analysis

Abstract

Chatter monitoring is an essential requirement for high-precision machining systems. Chatter vibration is characterized by amplitude modulation when the beat-frequency effect or geometric eccentricities exist, which hinders methods based on the transient characteristic index. Here, moving average difference spectrum analysis (MADSA) is proposed for detection of modulated chatter; the method can detect chatter frequency and track its amplitude from low signal-to-noise vibration signals early on. MADSA utilizes a moving average to smooth the amplitude trend of chatter frequency candidates obtained by the difference spectrum analysis, and the chatter frequency is detected using monotonicity analysis. MADSA was validated in both simulations and gear-milling machine experiments, and comparisons between wavelet packet-energy entropy method and MADSA are described, to illustrate the performance of the latter.