Chatter detection in milling process based on synchrosqueezing transform of sound signals

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

This study proposes a high-resolution processing technique for seismic data based on the improved synchrosqueezing transform (SST). The SST rearranges the complex spectrum of the wavelet transform along the frequency axis. However, the energy is not concentrated in the position with the faster frequency change rate. To overcome these problems, the proposed method first transformed the seismic signal into an analytical one via Hilbert transformation and then determined the phase correction value (x0(t)) before and after the transformation. This was achieved using the instantaneous frequency attribute (ω(t)) of the analytical signal and a specific frequency (ω0) selected as the dominant frequency of the input data to adjust the change rate of the signal frequency before the SST falls close to zero, which can improve the time–frequency resolution after compression transformation. The essence of this method only adjusts the phase of the signal before and after transformation, without affecting the inverse expression of the SST. The one- and two-dimensional model data results show that the proposed method can better identify the time–frequency distribution characteristics of seismic signals and improve their resolution and the focusing effect in the time–frequency domain. The proposed method shows good prospects for application in gas reservoir detection and identification.

When seismic waves encounter abrupt points of stratum or lithology in the process of propagation, such as fault edges, pinch-out points, or the protrusion of unconformity surfaces, the Snell law will break down and many diffractions will be generated. However, the diffraction information is typically masked by strong reflections; thus, separating diffractions is one key issue for diffraction imaging. The traditional singular value decomposition (SVD) method with the ability for wavefield decomposition and reconstruction is useful in removing strong reflections of large singular values. However, the continuously changing characteristics of the singular value of steep-slope reflections make it difficult to choose a suitable parameter for separating diffractions. To solve the problem of singular value selection in diffractions reconstruction by the SVD method, we have developed a Cook-distance SVD method by mathematically least-squares measuring the contribution of every singular value. The Cook-distance SVD can amplify the difference between continuously changing singular values and easily find the singular values representing diffractions. Synthetic examples and field data applications demonstrate that our Cook-distance SVD method can avoid parameter testing and has good performance in diffraction separation and can highlight reservoir-involved small-scale geologic edges and scatterers.

Synchrosqueezing transform (SST) is an effective time-frequency analysis (TFA) approach for the processing of nonstationary signals. The SST shows a satisfactory ability of the TF localization of the nonlinear signal with a slowly time-varying instantaneous frequency (IF). However, for the signal of which ridge curves in the TF domain are fast varying, or even almost parallel to the frequency axis, the SST will provide a blurred TF representation (TFR). To solve this issue, the transient-extracting transform (TET) was recently put forward. The TET can effectively characterize and extract transient features in the much concentrated TFR for the strongly frequency-modulated (FM) signal, especially the impulse-like signal. However, contrary to the SST, it is not suitable for weak FM modes. In this study, we propose a TFA method called the time-synchroextracting general chirplet transform (TEGCT). The TEGCT can achieve a highly concentrated TFR for strong FM signals as well as weak FM ones. Quantized indicators, the concentration measurement and the peak signal-to-noise ratio, are used to analyze the performances of the proposed method compared with those of other methods. The comparisons show that the TEGCT can provide a result with better TF localization. Then, the proposed method was applied to the spectrum analysis of the seismic data for oil reservoir characteristics. The horizontal slices of the offshore 3-D seismic data show that the TEGCT delineates more distinct and continuous subsurface channels in a fluvial-delta deposition system. All the results illustrate that our proposed method is a good potential tool for seismic processing and interpretation in the geoscience.

The denoising for the microseismic signals in the strong background interferences during the microseismic monitoring of hydraulic fracturing is very important for events identification and first-arrival picking. As characteristics of microseismic signals are normally random, non-stationary, and time-varying, a powerful tool for weak signal extraction of microseismic data is still a great challenge. In this study, a denoising method for microseismic signals is proposed based on the synchrosqueezing transform in the time-frequency domain. Firstly, the microseismic signals are denoised by the adaptive threshold method in the time-frequency domain by the synchrosqueezing transform. Then, the frequency center of the effective signal is integrated and extracted in the time-frequency domain. At last, the weak signal is extracted by the reconstruction of synchrosqueezing transform. The results from the synthesized non-stationary signal with different noise intensities and the real microseismic single of a track record show that this method has better anti-noise ability and higher signal extraction accuracy. Moreover, according to the processing and analysis of microseismic data in field wells, it is proved that this denoising method has a good practical value and can extract the effective and high-precision signals in the strong noise background.

Synchrosqueezing transform (SST) benefits from an instantaneous frequency (IF) estimator in the time-frequency domain, providing an energy-concentrated time-frequency representation to describe the time-varying frequency of seismic signals. To enhance the concentration performance of SST, this paper theoretically proposes a statistical SST (SSST) by constructing a spectrum-weighted IF estimator in the short-time Fourier transform (STFT) domain. In this SSST, a linear chirp signal is introduced to better capture its chirp rate, and then the quadratic STFT spectra are weighted by a window-related weighting function. On this basis, two equations related to the IF and chirp rate are constructed to derive the spectrum-weighted IF estimator. Finally, the STFT coefficients are squeezed to the estimated IF trajectories by a frequency fixed-point iterative algorithm, thereby providing a more concentrated time-frequency representation for multi-component signals than existing advanced methods, while enabling to retrieve each component. Two synthetic examples and one field seismic data on thin interbeds are utilized to demonstrate the effectiveness of the proposed SSST and show its ability to highlight the time-varying frequency features of seismic signals, which is a promising seismic data analysis tool, such as characterizing thin interbed thickness variations.

An improved ridge estimation (IRE) method for troposphere water vapor tomography

There are various analysis methods for CT perfusion (CTP). Although the advantages of Bayesian estimation algorithms have been newly suggested, comparisons with other analysis methods on clinical data are still limited. In this study, we compared the Bayesian estimation method with the singular value decomposition (SVD) method in the evaluation of patients with acute cerebral infarction and examined its usefulness. CTP data from 13 patients with acute stroke were analyzed using the SVD and Bayesian estimation methods implemented in Vitrea. Evaluation of visual clarity of the ischemic area and quantitative values of the healthy side-affected side ratio using the mean values of the left and right region of interest (ROI) on the images were compared using the SVD and Bayesian estimation methods. In visual evaluation, there were significant differences in CBV in four cases, and in CBF, MTT, and TTP in many cases. The healthy side-affected side ratio of the SVD and Bayesian estimation methods were as follows: CBF 1.19, 1.84; CBV 1.09, 1.02; MTT 1.12, 1.79; and TTP 1.48, 1.19. For CBF and MTT, the Bayesian estimation method had a larger ratio of the healthy side to the affected side, and for TTP, the SVD method had a larger ratio of the test side to the affected side. We suggest that the Bayesian estimation method is more useful than the SVD method for assessing CBF and MTT in CTP analysis of patients with acute stroke.

A new method for evaluating the decay ratios in a boiling water reactor (BWR) using the singular value decomposition (SVD) method had been proposed. In this method, a signal component closely related to the BWR stability can be extracted from independent components of the neutron noise signal decomposed by the SVD method. However, real-time stability monitoring by the SVD method requires an efficient procedure for screening such compoments. For efficient screening, an artificial neural network (ANN) with three layers was adopted. The trained ANN was actually applied to decomposed components of local power range monitor (LPRM) signals that were measured in stability experiments conducted in the Ringhals-1 BWR. In each LPRM signal, multiple candidates were screened from the decomposed components. However, decay ratios could be estimated by introducing appropriate criterions for selecting the most suitable component among the candidates. The estimated decay ratios are almost identical to those evaluated by visual screening in a previous study. The selected components commonly have the largest singular value, the largest decay ratio and the least squared fitting error among the candidates. By virtue of excellent screening performance of the trained ANN, the real-time stability monitoring by the SVD method can be applied in practice.

The traditional singular value decomposition (SVD) method is unable to diagnose the weak fault feature of bearings effectively, which means, it is difficult to retain the effective singular components (SCs). Therefore, a new singular value decomposition method, SVD based on the FIC (fault information content), is proposed, which takes the amplitude characteristics of fault feature frequency as the selection index FIC of singular components. Firstly, the Hankel matrix of the original signal is constructed, and SVD is applied in the matrix. Secondly, the proposed index FIC is used to evaluate the information of the decomposed SCs. Finally, the SCs with fault information are selected and added to obtain the denoised signal. The results of bearing fault simulation signals and experimental signals show that compared with the traditional differential singular value decomposition (DS-SVD), the proposed method can select the singular components with larger amount of fault information and is able to diagnose the fault under the heavy noise interference. The new method can be used for signal denoising and weak fault feature extraction.

Pretreatment ischemic core volume is conceptually equal to follow-up infarct volume (FIV) in patients with successful recanalization. However, there is sometimes an absolute volume difference (AD) between pretreatment core volume and FIV. The aim was to compare the AD values between the Bayesian and the singular value decomposition (SVD) methods with time from onset-to-imaging in acute ischemic stroke (AIS) patients undergoing mechanical thrombectomy. Consecutive AIS patients were included if they had the following: (1) anterior large vessel occlusion (internal carotid or middle cerebral artery); (2) within 24h of onset; (3) pretreatment CT perfusion (CTP); (4) successful recanalization (mTICI ≥ 2b); and (5) 24-h diffusion-weighted imaging (DWI). FIV was measured on 24-h DWI. The AD value between FIV and the pretreatment core volume was calculated for Bayesian and SVD methods. Spearman's rank correlation coefficient (rho) was calculated as appropriate. In the 47 patients enrolled (25 men; median age 78years; median baseline National Institutes of Health Stroke Scale, 22), the median time from onset-to-imaging and onset-to-recanalization was 136 and 220min, respectively. Shorter onset-to-imaging time was correlated with a larger AD value, and more trend was seen in the SVD method (rho = - 0.28, p = 0.05) compared with the Bayesian method (rho = - 0.08). A larger pretreatment core volume was correlated with a larger AD value, and this tendency was slightly stronger for the SVD (rho = 0.63, p < 0.01) than for the Bayesian (rho = 0.32, p = 0.03) method. The Bayesian method might be more correlated with FIV than the SVD method in patients with a large ischemic lesion immediately after stroke onset, but not perfect.

Chatter detection methods in the machining processes: A review

Chatter causes machining instability and reduces productivity in the metal cutting process. It has negative effects on the surface finish, dimensional accuracy, tool life and machine life. Chatter identification is therefore necessary to control, prevent, or eliminate chatter and to determine the stable machining condition. Previous studies of chatter detection used either model-based or signal-based methods, and each of them has its drawback. Model-based methods use cutting dynamics to develop stability lobe diagram to predict the occurrence of chatter, but the off-line stability estimation couldn’t detect chatter in real time. Signal-based methods apply mostly Fourier analysis to the cutting or vibration signals to identify chatter, but they are heuristic methods and do not consider the cutting dynamics. In this study, the model-based and signal-based chatter detection methods were thoroughly investigated. As a result, a hybrid model- and signal-based chatter detection method was proposed. By analyzing the residual between the force measurement and the output of the cutting force model, milling chatter could be detected and identified efficiently during the milling process.

To determine the most suitable postprocessing technique for magnetic resonance (MR) perfusion imaging in patients with vascular stenosis, by comparing the cerebral blood flow (CBF) maps of single photon emission tomography (SPECT) and perfusion MR imaging (MRI). In 15 consecutive patients (14 men and one woman, mean age 73.9 +/- 6.0 years) with stenosis of common carotid artery (CCA) or internal carotid artery (ICA) of more than 75%, both brain perfusion MRI and brain perfusion SPECT were performed. From perfusion MR images, CBF maps were calculated with the first moment, singular value decomposition (SVD), and block circulant SVD (b-SVD) methods, and CBF maps from each algorithm were compared with those from SPECT. The b-SVD method had the best correlation with SPECT (R = 0.814), followed by the first moment method (R = 0.776) and the SVD method (R = 0.723). The b-SVD method has the least mean difference with SPECT (0.118), the first moment method also had less difference (0.121), and the SVD had greatest mean difference (0.164). Our results suggest that in patients with vascular impairment the b-SVD method will be the technique of choice rather than SVD or first moment method.

The main factors responsible for the nonstationarity of seismic signals are the nonstationarity of the geologic structural sequences and the complex pore structure. Time-frequency analysis can identify various frequency components of seismic data and reveal their time-variant features. Choosing a proper time-frequency decomposition algorithm is the key to analyze these nonstationarity signals and reveal the geologic information contained in the seismic data. According to the Heisenberg uncertainty principle, we cannot obtain the finest time location and the best frequency resolution at the same time, which results in the trade-off between the time resolution and the frequency resolution. For instance, the most commonly used approach is the short-time Fourier transform, in which the predefined window length limits the flexibility to adjust the temporal and spectral resolution at the same time. The continuous wavelet transform (CWT) produces an “adjustable” resolution of time-frequency map using dilation and translation of a basic wavelet. However, the CWT has limitations in dealing with fast varying instantaneous frequencies. The synchrosqueezing transform (SST) can improve the quality and readability of the time-frequency representation. We have developed a high-resolution and effective time-frequency analysis method to characterize geologic bodies contained in the seismic data. We named this method the SST, and the basic wavelet is the three-parameter wavelet (SST-TPW). The TPW is superior in time-frequency resolution than those of the Morlet and Ricker wavelets. Experiments on synthetic and field data determined its validity and effectiveness, which can be used in assisting in oil/gas reservoir identification.