Non-stationary machine condition monitoring is very important in modern automated manufacturing processes. In this research, an innovative non-stationary (transient) signal analysis approach has been developed for non-stationary machine condition monitoring. It is based on time-frequency distributio
Analysis of signals for monitoring of nonlinear and non-stationary machining processes
β Scribed by Tomas Kalvoda; Yean-Ren Hwang
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 936 KB
- Volume
- 161
- Category
- Article
- ISSN
- 0924-4247
No coin nor oath required. For personal study only.
β¦ Synopsis
An investigation of cutting process monitoring based on dynamic force and acceleration signals in the frequency and time-frequency domains is presented in this paper. The performance of a new data analysis technique, the Hilbert-Huang Transform (HHT), is used to analyze this process in frequency and time-frequency domain. This technique is also compared with the traditional Fourier transform method power spectra in the frequency domain approach. A comparison is made of the analysis of two commonly used signals: acceleration and dynamic force. The shift of the main frequency peak into lower frequencies and higher frequency fluctuations is considered as a cutter tool wear indicator. The appearance of new frequency indicates a cutter tool fault. The performance of dynamic cutting force signal using both HHT and Fourier transform shows better results within this study. The HHT (which deals with nonlinear and non-stationary signals) has been shown to be a robust tool for estimating cutter tool wear/fault.
π SIMILAR VOLUMES
Monitoring of machining processes is a classic and yet unsolved problem in manufacturing engineering. This paper introduces a new method of feature extraction and feature assessment using a wavelet packet transform for monitoring of machining processes. First, the principles of wavelet transforms ar