Traditional Time Domain Analysis Research Articles

Analysis of critical states based on acoustic emission signals during progressive failure of wood.

The analysis of critical states during fracture of wood materials is crucial for wood building safety monitoring, wood processing, etc. In this paper, beech and camphor pine are selected as the research objects, and the acoustic emission signals during the fracture process of the specimens are analyzed by three-point bending load experiments. On the one hand, the critical state interval of a complex acoustic emission signal system is determined by selecting characteristic parameters in the natural time domain. On the other hand, an improved method of b_value analysis in the natural time domain is proposed based on the characteristics of the acoustic emission signal. The K-value, which represents the beginning of the critical state of a complex acoustic emission signal system, is further defined by the improved method of b_value in the natural time domain. For beech, the analysis of critical state time based on characteristic parameters can predict the "collapse" time 8.01 s in advance, while for camphor pines, 3.74 s in advance. K-value can be analyzed at least 3 s in advance of the system "crash" time for beech and 4 s in advance of the system "crash" time for camphor pine. The results show that compared with traditional time-domain acoustic emission signal analysis, natural time-domain acoustic emission signal analysis can discover more available feature information to characterize the state of the signal. Both the characteristic parameters and Natural_Time_b_value analysis in the natural time domain can effectively characterize the time when the complex acoustic emission signal system enters the critical state. Critical state analysis can provide new ideas for wood health monitoring and complex signal processing, etc.

A multi-energy load forecasting method based on complementary ensemble empirical model decomposition and composite evaluation factor reconstruction

Ensuring precise multi-energy load forecasting is crucial for the effective planning, management, and operation of Integrated Energy Systems (IES). This study proposes a novel multivariate load forecasting model based on time-series decomposition and reconstruction to handle the complex, high-dimensional multi-energy load data in IES and enhance forecasting accuracy. Initially, the model conducts a thorough correlation analysis and variable screening to minimize irrelevant data interference. It then applies denoising by decomposing the load sequence into modal components with distinct characteristics, using the complementary ensemble empirical mode decomposition (CEEMD). To overcome the unstable prediction accuracy inherent in time-domain decomposition methods, this study introduces an innovative composite evaluation factor (CEF) that reconstructs the modal components after considering their complexity, coupling, and frequency. The final predictions are generated using the proposed MTL-CNN-BiLSTM model, optimized with the attention mechanism. The results show that the proposed model significantly reduces error accumulation compared to traditional time-domain analysis methods, achieving a 37.40% reduction in average forecasting error and a 30.73% increase in forecasting efficiency.

Lost circulation detection method based on cepstrum analysis of transient pressure waves

Encountering natural fractures or unbalanced formation pressure during oil drilling can result in economic loss and environmental pollution due to well leakage. Existing detection methods encounter challenges such as high costs, complex downhole environments, and difficult data acquisition. To address these issues, we propose a well leakage detection method using cepstrum for analyzing transient pressure waves. Cepstrum is a signal Fourier transform after logarithmic operation and then Fourier inverse spectrum obtained. By studying the propagation of transient pressure waves in the wellbore, we identify drilling fluid leakage location and amount based on time-dependent and amplitude changes of pressure wave signal characteristic peaks. To handle noise in the pressure wave signal, we employ adaptive noise-complete ensemble empirical modal decomposition (CEEMDAN) and wavelet threshold (WT) joint denoising. Correlation coefficient (CCF) with the Hilbert joint spectrum (HJS) is used to extract main frequency components, achieving denoising. Experimental results confirm: ① Noise interference in transient pressure waves is effectively suppressed using the CEEMDAN-WT-CCF-HJS denoising method. ② Cepstrum analysis of the pressure wave signal during wellbore annulus system leakage reveals distinct reflected wave characteristic peaks, aiding in locating different leakage points, with the amplitude of these peaks reflecting the size of the leakage. ③ This method efficiently utilizes time-frequency information from the excitation pressure wave signal, offering advantages over traditional time-domain and frequency-domain analysis. Experiments covering various leakage scenarios, amounts, and borehole sizes yielded controlled experimental errors (2.25%–9.10%), within a reasonable range. The method's validity and reliability were confirmed, providing theoretical support and technical guidance for well leakage detection in oil drilling.

Visual analytics for process monitoring: Leveraging time-series imaging for enhanced interpretability

In the era of big data driven by the advent of the Internet of Things (IoT), process industries face the challenge of analyzing massive and complex data to extract relevant information for effective process monitoring. Despite exploring various approaches, scalability and interpretability continue to present practical limitations. To address these limitations, we propose a new framework called visual analytics. Visual analytics offers a new perspective on solving process monitoring problems. It involves transforming historical process data into visual clues, thereby converting traditional fault detection problems into image classification problems. This approach allows process experts to visually analyze patterns and textures within the data, making interpretation much easier compared to traditional time domain analysis. Moreover, by treating process data as images, visual analytics can leverage a wide range of computer vision techniques, including convolutional neural networks (CNNs), to accurately classify and detect faults. By integrating human visual perception with advanced computer vision techniques, visual analytics enables the effective analysis of massive and complex process data. To empirically validate the proposed visual analytics approach, we conduct experiments on both the simulated continuous stirred tank heater (CSTH) benchmark and the industrial arc loss benchmark. The experimental results from both benchmarks demonstrate that the proposed visual analytics approach yields competitive performance in predicting process faults while enhancing interpretability by providing meaningful and informative visual representations.

Association of heartbeat complexity with survival in advanced non-small cell lung cancer patients.

Previous studies have shown that the predictive value of traditional linear (time domain and frequency domain) heart rate variability (HRV) for the survival of patients with advanced non-small cell lung cancer (NSCLC) is controversial. Nonlinear methods, based on the concept of complexity, have been used to evaluate HRV, providing a new means to reveal the physiological and pathological changes in HRV. This study aimed to assess the association between heartbeat complexity and overall survival in patients with advanced NSCLC. This study included 78 patients with advanced NSCLC (mean age: 62.0 ± 9.3 years). A 5-min resting electrocardiogram of advanced NSCLC patients was collected to analyze the following HRV parameters: time domain indicators, i.e., standard deviation of the normal-normal intervals (SDNN) and root mean square of successive interval differences (RMSSD); frequency domain indicators, i.e., total power (TP), low frequency power (LF), high frequency power (HF), and the ratio of LF to HF (LF/HF); nonlinear HRV indicators characterizing heartbeat complexity, i.e., approximate entropy (ApEn), sample entropy (SampEn), and recurrence quantification analysis (RQA) indexes: mean diagonal line length (Lmean), maximal diagonal line length (Lmax), recurrence rate (REC), determinism (DET), and shannon entropy (ShanEn). Univariate analysis revealed that the linear frequency domain parameter HF and nonlinear RQA parameters Lmax, REC, and DET were significantly correlated with the survival of advanced NSCLC patients (all p < 0.05). After adjusting for confounders in the multivariate analysis, HF, REC, and DET were found to be independent prognostic factors for the survival of patients with advanced NSCLC (all p < 0.05). There was an independent association between heartbeat complexity and survival in advanced NSCLC patients. The nonlinear analysis method based on RQA may provide valuable additional information for the prognostic stratification of patients with advanced NSCLC and may supplement the traditional time domain and frequency domain analysis methods.

Research of Fault Diagnosis of Mine Rolling Bearing Based on Time-Frequency Analysis

Considering the shortcomings of traditional time-domain and frequency-domain analysis in processing non-stationary signals, this paper proposes to introduce time-frequency into the analysis of a one-dimensional vibration signal and transform it into a two-dimensional time-frequency image to obtain more abundant diagnostic information. At the same time, considering the problem of noise interference under complex working conditions, wavelet denoising is used to preprocess the signal, and the simulation signal is defined. The method of obtaining the time-frequency image verifies the denoising effect. Finally, through the public test data, it is proved that the method can effectively obtain reliable time-frequency images for further bearing fault diagnosis research.

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals.

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

A Recognition Method for Time-domain Waveform Images of Electric Traction Network Overvoltage Based on Deep Learning

A large amount of time-domain waveform images of overvoltage of electric traction network are collected during the on-board test of EMU. The traditional time-domain or frequency-domain analysis and classification method can’t be directly applied to time-domain waveform images. In order to analyze these images, firstly, image preprocessing is carried out. Then, the data set of time-domain waveform images is established. Finally, the CNN (convolutional neural network) classification model is trained based on data set. The results show that the sensitivity of recognition is above 81%, and this method is suitable for the time-domain waveform image of overvoltage of electric traction network.

An improved intrinsic time-scale decomposition method based on adaptive noise and its application in bearing fault feature extraction

This study proposes a new method for detecting and diagnosing fault information for rolling bearings. First, a novel method called complete ensemble intrinsic timescale decomposition with adaptive noise is proposed. Compared with the complete ensemble empirical modes decomposition with adaptive noise (CEEMDAN) method, the proposed method reduces the spurious mode, which interferes with the identification of feature frequency in rolling bearings. In addition, when a typical mode mixing signal is decomposed, the mode mixing problem that appears in the adjacent mode components of the CEEMDAN method is also solved. Second, there is only an intrinsic mode function, which mostly reflects the vibration feature of a rolling bearing. We propose a new method to evaluate these mode functions to determine which mode function contains the most useful information. Our proposed method solves the problem using traditional time-domain analysis methods of a single dimensionless parameter with poor stability and low sensitivity. Our method includes sample entropy, kurtosis, the zero-crossing rate, and a correlation coefficient. The proposed method reduces the limitations of each method to select the sensitive component. Finally, we use a simulation signal and an experimental signal to verify the effectiveness of the proposed method.

Novel method to operation conditions identification of high-order power converters

IntroductionIntermittent solar energy causes different operational modes of power converters including continuous current modes (CCMs) and discontinuous current modes (DCMs), which need appropriate control strategies and parameters assignment to ensure the functionality of the overall solar energy power generation system. Hence, it is important to identify suitable operation modes for a high-order converter system. However, for a high-order power converter (HOPC), traditional time-domain analysis method and bifurcation analysis are inapplicable, since this requires comprehensive analysis and sophisticated control design. ObjectivesTo improve reliability and reduce mathematical complexity, this paper focuses on the operation mode derivation of HOPCs to well identify its boundary conditions and provide industry standards for converter applications. MethodsWith complex operation modes, 3-Z-network converter is analysed as a typical example and its derivations of boundary conditions are elaborated. In detail, the equilibrium points and boundary conditions of each operation modes are first derived; then with the guidance of boundary conditions, unexpected operation modes can be avoided by parameters reassignment. ResultsSimulations and experimentation on the newly established system prototype are conducted to validate the effectiveness of the proposed approach. It demonstrates that the theoretical and experimental boundary conditions are in good agreement. ConclusionThis paper provides equilibrium points and boundary conditions, and obtains deeper insights into the behaviors of the 3-Z-network converter. The derivations of four operation modes and the boundary condition of each mode has been conducted and provided for the large-signal averaged model of the converter, which provides guidance for engineers to adjust the system parameters so as to realize required operation modes. Simulation and experimentation have verified the accuracy and effectiveness of the proposed identified operation boundaries.

Development of a Blended Time-Domain Program for Predicting the Motions of a Wave Energy Structure

Traditional linear time-domain analysis is used widely for predicting the motions of floating structures. When it comes to a wave energy structure, which usually is subjected to larger relative (to their geometric dimensions) wave and motion amplitudes, the nonlinear effects become significant. This paper presents the development of an in-house blended time-domain program (SIMDYN). SIMDYN’s “blend” option improves the linear option by accounting for the nonlinearity of important external forces (e.g., Froude-Krylov). In addition, nonlinearity due to large body rotations (i.e., inertia forces) is addressed in motion predictions of wave energy structures. Forced motion analysis reveals the significance of these nonlinear effects. Finally, the model test correlations examine the simulation results from SIMDYN under the blended option, which has seldom been done for a wave energy structure. It turns out that the blended time-domain method has significant potential to improve the accuracy of motion predictions for a wave energy structure.

Frequency-domain analysis method for analyzing and improving the steady-state characteristics of microcantilever in tapping-mode atomic force microscopy

In tapping-mode AFM, the steady-state characteristics of microcantilever are extremely important to determine the AFM performance. Due to the external excitation signal and the tip-sample interactions, the solving process of microcantilever motion equation will become very complicated with the traditional time-domain analysis method. In this paper, we propose the novel frequency-domain analysis method to analyze and improve the steady-state characteristics of microcantilever. Compared with the previous methods, this new method has three prominent advantages. Firstly, the analytical expressions of amplitude and phase of cantilever system can be derived conveniently. Secondly, the stability of the cantilever system can be accurately determined and the stability margin can be obtained quantitatively in terms of the phase margin and the magnitude margin. Thirdly, on this basis, external control mechanism can be devised quickly and easily to guarantee the high stability of the cantilever system. With this novel method, we derive the frequency response curves and discuss the great influence of the intrinsic parameters on the system stability, which provides theoretical guidance for selecting samples to achieve better AFM images in the experiments. Moreover, we introduce a new external series correction method to significantly increase the stability margin. The results indicate that the cantilever system is no longer easily disturbed by external interference signals.

Time color map and histogram of electromyography (EMG) sample amplitudes: possible tools for global electromyogram analysis by images

To present a method for surface electromyogram (EMG) analysis by images. 12 healthy sedentary males were studied. Surface EMG (from lateral deltoid) was recorded (1000 samples/s) and analysed during self-selected angular speed shoulder abduction/adduction actions, made at 40% of 1 revolution maximum, up to exhaustion. EMG was rectified and normalized to its maximum (100%). Histogram of sample amplitudes: the 0–100% amplitude range was divided in 20 levels (step 5%). Each sample was assigned to its bin for the histogram generation 2D time color map: the amplitude range was divided in five colored levels: blue (0–20%), green (21–40%), yellow (41–60%), orange (61–80%), red (81–100%). Along the time bar each ms was colored according to the sample relative amplitude. The whole colored bar was divided into 100 ms slices. These short color bars were graphically stacked one on top of each other and interpolated on the x/y plane using a standard contour plotting procedure. The spike shape analysis (SSA) was performed and the average rectified value (ARV) was calculated. From the first to the last action the histogram median amplitude increased from 34.33 ± 12.36 to 46.74 ± 14.04%. On the 2D map the areas of activity hues enlarged in both directions showing more regular recurrence of longer electrical events. As expected, the ARV increased and the SSA reflected the spikes changes at fatigue. Histogram of the EMG sample amplitudes and 2D time color map provide data, at a glance, about the regularity of the EMG spikes occurrence and their amplitude distribution. This is a different, adjunct information with respect to the one provided by traditional EMG time domain analysis measurements.

Compressed sensing: Radar signal detection and parameter measurement for EW applications

AbstractState of the art system development is very much required for UAVs (Unmanned Aerial Vehicle) and other airborne applications, where miniature, lightweight and low‐power specifications are essential. Currently, the airborne Electronic Warfare (EW) systems are developed with digital receiver technology using Nyquist sampling. The detection of radar signals and parameter measurement is a necessary requirement in EW digital receivers. The Random Modulator Pre‐Integrator (RMPI) can be used for matched detection of signals using smashed filter. RMPI hardware eliminates the high sampling rate analog to digital computer and reduces the number of samples using random sampling and detection of sparse orthonormal basis vectors. RMPI explore the structural and geometrical properties of the signal apart from traditional time and frequency domain analysis for improved detection. The concept has been proved with the help of MATLAB and LabVIEW simulations.

THE RECENT USE OF HEART RATE VARIABILITY FOR RESEARCH

Heart rate variability (HRV) is a non-invasive method that analysis cardiac autonomic regulation1,2.The use of this method was previously validated in physiological experiments3,4,which performed pharmacological stimulation of the sympathetic and parasympathetic nervous systems and made relationship with the traditional time and frequency domain analysis of HRV, confirming the use of this method to evaluate cardiac autonomic regulation. Moreover, HRV has also been used to predict mortality after cardiacevents5 and it has been considered as a marker of stress6. An easy and economic access to HRV analysis through heart rate monitors, which are well recognized in the literature7, 8 helped researches to use this method to evaluate cardiac autonomic regulation.In the last 15 years this method has received great attention.After a careful search on Pubmed/Medline, the term “heart rate variability” was present in 1,013 manuscripts in 2014, in 2013 this number was 972 and in 2012 it was 7299. We realize that each year the number of groups that use HRV in their studies has considerably increased. In this context, I raise points that are worth to mention. Firstly, advantages includeadditional information for cardiac autonomic responses in pathological situations10-12, during physiological stress13,14, association with social function15 for instance. It is currently clear that autonomic nervous system is related to several conditions. Nevertheless, before starting to study HRV analysis,relevant details of this method should be discussed in order to avoid misunderstanding of results. For example, HRV consider the interval between consecutive heart beats2, reflecting parasympathetic and sympathetic cardiac autonomic modulation, it does not directly measures sympathetic or parasympathetic activity. There are different methods that quantify it, i.e. plasma levels of norepinephrine and epinephrine16, renal sympathetic nerve activity17and parasympathetic nerve activity18. In 1996, the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology published methodological considerations regarding traditional time and frequency domain analysis of HRV2. This seminal publication aimed to permit appropriate comparisons.Since then, new methods related to the chaos theory applied on organic systems and heart rate dynamics has been investigated19. This year the European Society of Cardiology together with the European Heart Rhythm Association and co-endorsed by the Asia Pacific Heart Rhythm Society reviewed the literature abovementioned in 199619. They addressed the use of non-conventional methods (nonlinear) which had been used in a significant number of patients (more than 200 cases) finding only 21 studies from the thousands already published. They described in details nonlinear methods as Detrended fluctuation analysis with a1 and a2 components, Hurst Exponent, Correlation Dimension and Largest Lyapunov Exponent. In this sense, I take this opportunity to reinforce attention of researchers around the world to keep being extremely careful when using HRV for their analysis. Important factors are necessary to consider, such as sample frequency to use when digitalizing the electrocardiogram, filtering records, caution with stationary data andwhether to interpolate an electrocardiogram to enhance accuracy of R wave. Therefore, investigators around the world will keep helping us to discover the unlimited mysteries of science. The current number of the Journal of Human Growth and Development is composed by a total of 15 original manuscripts. The journal keeps

Time-frequency analysis of wheel-rail shock in the presence of wheel flat

Abstract: Against the deficiencies of traditional time domain and frequency domain analysis in detecting wheel-rail (W-R) system hidden risks which wheel flats generate, the time-frequency characteristics of W-R shock caused by wheel flat are analyzed and the vehicle-rail dynamic model with wheel flat is investigated. The 10 degrees of freedom (DOF) vehicle model is built up. 90-DOF rail model is constructed. The wheel flat excitation model is built up. The vehicle-track coupling dynamic model including wheel flat excitation is set up through nonlinear Hertzian contact theory. The vertical accelerations of axle box are calculated at different speeds and flat sizes based on the vehicle-track coupling dynamic model with wheel flat. Frequency slice wavelet transform (FSWT) is employed to analyze time-frequency characteristics of axle box accelerations to detect the W-R noncontact risks s which the traditional time domain or frequency domain method does not analyze. The results show that the small flat size and high running speed lead to high frequency W-R impact. Large flat size and high running speed result in momentary loss of W-R contact, and there exist security risks between wheel and rail. The conclusion that the phase of axle box accelerations is same to W-R forces lays a theoretical foundation of monitoring W-R contact safety from axle box acceleration instead of traditional W-R force detection.

Electromyogram features during linear torque decrement and their changes with fatigue.

Surface electromyogram (EMG) spike shape analysis (SSA) has recently been proposed as an adjunct tool to EMG time and frequency domain analysis to increase our knowledge of motor unit (MU) control strategies. The study was aimed to understand more in MU deactivation strategy during torque decrement, and its possible changes in fatigued muscle, using a combination of traditional time and frequency domain analysis and SSA techniques. EMG was detected from the biceps brachii of 11 untrained male subjects during static down-going ramp contractions (90-0% of the maximal voluntary contraction, MVC) under non-fatigued (DGR) and fatigued (FDGR) conditions. The root mean square (RMS) and mean frequency (MF), as well as SSA parameters, were calculated on 1-s EMG windows centred on each 10 % MVC step for both conditions. In both the DGR and FDGR EMG-RMS, mean spike amplitude and mean spike slope decreased by 50 % in the 90-60 % MVC. The mean spike frequency also decreased by 50 % in the 30-10 % MVC. Except the "mean number of spikes per second" all the other estimated EMG parameters were significantly different during FDGR compared to DGR. The dynamics of EMG parameters during torque decrement would support a MU deactivation strategy which relies more on MU de-recruitment in the high % MVC range and more on firing rate reduction in the low % MVC range. The adopted integrated approach to EMG signal processing could indicate that SSA is an important tool to disclose alterations in motor control due to fatigue.

Search for HRV-parameters that detect a sympathetic shift in heart failure patients on β-blocker treatment

Background: A sympathetic shift in heart rate variability (HRV) from high to lower frequencies may be an early signal of deterioration in a monitored patient. Most chronic heart failure (CHF) patients receive β-blockers. This tends to obscure HRV observation by increasing the fast variations. We tested which HRV parameters would still detect the change into a sympathetic state.Methods and results: β-blocker (Carvedilol®) treated CHF patients underwent a protocol of 10 min supine rest, followed by 10 min active standing. CHF patients (NYHA Class II–IV) n = 15, 10m/5f, mean age 58.4 years (47–72); healthy controls n = 29, 18m/11f, mean age 62.9 years (49–78). Interbeat intervals (IBI) were extracted from the finger blood pressure wave (Nexfin®). Both linear and non-linear HRV analyses were applied that (1) might be able to differentiate patients from healthy controls under resting conditions and (2) detect the change into a sympathetic state in the present short recordings.Linear: mean-IBI, SD-IBI, root mean square of successive differences (rMSSD), pIBI-50 (the proportion of intervals that differs by more than 50 ms from the previous), LF, HF, and LF/HF ratio.Non-linear: Sample entropy (SampEn), Multiscale entropy (MSE), and derived: Multiscale variance (MSV) and Multiscale rMSSD (MSD). In the supine resting situation patients differed from controls by having higher HF and, consequently, lower LF/HF. In addition their longer range (τ = 6–10) MSE was lower as well. The sympathetic shift was, in controls, detected by mean-IBI, rMSSD, pIBI-50, and LF/HF, all going down; in CHF by mean-IBI, rMSSD, pIBI-50, and MSD (τ = 6–10) going down. MSD6–10 introduced here works as a band-pass filter favoring frequencies from 0.02 to 0.1 Hz.Conclusions: In β-blocker treated CHF patients, traditional time domain analysis (mean-IBI, rMSSD, pIBI-50) and MSD6–10 provide the most useful information to detect a condition change.

Arc-Fault Detection Method Research Based on Wavelet Transformation

The arc-fault is the main reason that cause electric fires. The technology of arc-fault circuit interrupters (AFCI) is the new circuit protection technology and it could avoid arc-fault causing fire effectively. The appearance of arc-fault can not be predicted. The traditional time domain or frequency domain analysis method for arc-fault signal processing is not ideal because it’s inaccurate and not in time. This paper bases on characteristics of arc-fault signals and analyzes the series connection arc-fault signal by Daubechies wavelet transform in 4 orders. As a result, it can provide the characteristics of arc-fault and detect arc-fault effectively and timely. This method is confirmed reliability by the simulation result and provides the theoretical basis of the development of AFCI.

Tool-Edge Wear and Wavelet Packet Transform Analysis in High-Speed Machining of Inconel 718

Tool-edge wear (i.e., the wear of a tool cutting edge before it is fully worn away) is among significant concerns in high-speed machining because it can result in early tool failure and deteriorated quality of machined parts. Based on extensive experimental results, this paper shows how tool-edge wear is correlated with the cutting forces and vibrations in high-speed turning of Inconel 718. The following research findings are made from the present study: 1) The development of tool-edge wear depends on the initial tool-edge geometry and the cutting conditions employed. 2) The amount of tool-edge wear varies at different measurement points along the tool cutting edge, and increases as the feed rate increases. 3) The effect of tool-edge wear on the cutting forces depends on the initial tool-edge geometry and the cutting conditions employed. 4) The traditional time-domain analysis based on the vibration amplitude is not helpful in explaining and showing the dynamic development of tool-edge wear, and wavelet packet transform helps in identifying the changes in the vibration signals in different frequency bands.