Chatter is a kind of self-excited vibration and causes negative effects in machining processes. This paper presents a practical method to identify the chatter with cutting force signals in milling processes. Since the spectrum of the chatter signal exhibits discrete spectral lines around the chatter frequencies and the Renyi entropy is an effective index to characterize the randomness of data series, the frequency-domain Renyi entropy is proposed as a chatter indicator. As the chatter severity level grows, the signal components at the chatter frequencies become more and more significant, which means a reduction of the randomness of the spectral series. As a result, the value of the Renyi entropy-based indicator decreases rapidly at the onset of the chatter. In order to eliminate the interference of the normal signal components, i.e., the spindle speed-related frequency components, the spectrum is preprocessed to filter out those components first. Various milling experiments are conducted. The results show that the value of the proposed indicator changes sharply at the onset of chatter in various milling conditions with different spindle speeds and cutting depths. Also, the proposed indicator is compared with the commonly used Shannon entropy-based indicator and verified to have a larger difference between the stable and chatter statuses and is higher sensitivity to the chatter.

Chatter detection in milling processes using frequency-domain Rényi entropy

Bayesian nonparametric estimation of test equating functions with covariates

The application of the improved empirical mode decomposition (EMD) theory in gearbox fault diagnosis has been studied in this paper, and the transient features of gearbox vibration signals are shown. Based on using EMD, an improved algorithm of orthogonal empirical mode decomposition (OEMD) is put forward and is applied to extract the fault feature. Finally, fault diagnosis application in a gearbox is used as an example to prove the feasibility of the proposed method.

/pdf/fault-diagnosis-for-gearbox-based-on-improved-empirical-mode-5a0zj9ygp1.pdf

Fault Diagnosis for Gearbox Based on Improved Empirical Mode Decomposition

/pdf/adaptive-iterative-filtering-methods-for-nonlinear-signal-rsqkf4cucu.pdf

Adaptive iterative filtering methods for nonlinear signal analysis and applications

Aiming at the problem of electromagnetic interference signal separation, we propose a single-channel blind source separation method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA). Firstly, decompose the mixed interference signal by CEEMDAN to obtain a series of intrinsic mode functions (IMF). Secondly, combine them into a new multi-dimensional signal and solve its correlation matrix, and use singular value decomposition to obtain the eigenvalues of the matrix, and then use it to estimate the source number. Thirdly, select those IMFs whose correlation coefficients with the observed signal are bigger and regard them together with the original signal after denoising as new observed signals. Finally, recover the source signals by fast independent component analysis (Fast-ICA). We also extend our method to multi-channel BBS, and experiment results show that our method can eliminate unnecessary noise in the signal and perform well.

A New Method for Separating EMI Signal Based on CEEMDAN and ICA

Empirical Mode Decomposition (EMD) is an adaptive decomposition method developed in non-stationary signal processing. But one of the main drawbacks of the EMD is the appearance of mode-mixing when high frequency components in a signal contain intermittence in time domain. The paper discusses two new mode-mixing resistant methods: frequency heterodyne EMD method and masking signal EMD method. The principle and the operating steps of these methods are studied in detail, as well as their comparison. The simulation and application in a backlash nonlinearity system shows that both of them can successfully solve the mode-mixing problem in normal EMD method.

Study on two mode-mixing resistant empirical Mode Decomposition methods

Empirical Mode Decomposition (EMD) is a non-stationary signal processing method developed recently. It has been applied in many engineering fields. EMD has many similarities with wavelet decomposition. But EMD Decomposition has its own characteristics, especially in accurate trend extracting. Therefore the paper firstly proposes an algorithm of extracting slow-varying trend based on EMD. Then, according to wavelet regression estimation method, a new regression function estimation method based on EMD is presented. The simulation proves the advantages of the approach with easy computation and more accurate result.

Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition

To distinguish chatter gestation, chatter recognition method based on hybrid KPCA(Kernel Principal Compenent Analysis) and SVM(Support Vector Machine) is proposed for dynamic patterns of chatter gestation in cutting process. At first, FFT features are extracted from the vibration signal of cutting process, then FFT vectors are presorted and introduced to KPCA-SVM for machine learning and classification. Finally the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

Wen Long Li

Papers

Study on two mode-mixing resistant empirical Mode Decomposition methods

Fully Nonparametric Regression Estimation Based on Empirical Mode Decomposition

Hybrid KPCA-SVM Method for Pattern Recognition of Chatter Gestation