Abstract
Grinding chatter reduces the long-term reliability of grinding machines. Detecting the negative effects of chatter requires improved chatter detection techniques. The vibration signals collected from grinders are mainly nonstationary, nonlinear and multidimensional. Hence, bivariate empirical mode decomposition (BEMD) has been investigated as a multiple signal processing method. In this paper, a feature vector extraction method based on BEMD and Hilbert transform was applied to the problem of grinding chatter. The effectiveness of this method was tested and validated with a simulated chatter signal produced by a vibration signal generator. The extraction criterion of true intrinsic mode functions (IMFs) was also investigated, as well as a method for selecting the most ideal number of projection directions using the BEMD algorithm. Moreover, real-time variance and instantaneous energy were employed as chatter feature vectors for improving the prediction of chatter. Furthermore, the combination of BEMD and Hilbert transform was validated by experimental data collected from a computer numerical control (CNC) guideway grinder. The results reveal the good behavior of BEMD in terms of processing nonstationary and nonlinear signals, and indicating the synchronous characteristics of multiple signals. Extracted chatter feature vectors were demonstrated to be reliable predictors of early grinding chatter.
Highlights
Grinders, which are often high-precision, large-tonnage machines, are essential in the equipment manufacturing industry
The process consists of the following steps: (1) signal acquisition and pre-processing, such as signal denoising and reconstruction; (2) applying the bivariate empirical mode decomposition (BEMD) and extraction criterion of true intrinsic mode functions (IMFs) to obtain all true IMFs; (3) applying the Hilbert transform to true IMFs to obtain Hilbert and marginal spectrum distributions; (4) calculating the real-time variance and instantaneous energy of IMFs, and superposing and normalizing the feature vectors, respectively; and (5) predicting grinding chatter by contrasting the changes of the feature vectors
Bivariate empirical mode decomposition (BEMD) and Hilbert transform were demonstrated as reliable methods for processing simulated and experimental chatter signals
Summary
Grinders, which are often high-precision, large-tonnage machines, are essential in the equipment manufacturing industry. In practice, grinder vibration signals are usually multidimensional signals, yet current methods are only useful with one-dimensional, real-value time series [21] They are unfavorable for executing information fusion functions and accurately reflecting real-world situations when processing multiple signals. The authors of this paper have made a comparison between the use of EMD and BEMD for extracting features from grinding chatter signals, and demonstrated the superior performance of BEMD [28] These findings can be summarized as follows:. Feature Vector Extraction Method Based on Bivariate Empirical Mode Decomposition (BEMD)
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