On-line monitoring of WEDM-LS surface roughness by utilizing time-frequency characteristics of AE signal

2017 ◽  
Vol 25 (8) ◽  
pp. 2090-2097
Author(s):  
王 艳 WANG Yan ◽  
王健波 WANG Jian-bo ◽  
王 强 WANG Qiang ◽  
熊 巍 Xiong Wei ◽  
贺独醒 HE Du-xing
Keyword(s):  
Surface Roughness ◽  
Frequency Characteristics ◽  
Time Frequency ◽  
On Line ◽  
Ae Signal

scholarly journals A Time-Frequency Based Approach for Acoustic Emission Assessment of Sliding Wear

Lubricants ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 52
Author(s):  
Igor Rastegaev ◽  
Dmitry Merson ◽  
Inna Rastegaeva ◽  
Alexei Vinogradov
Keyword(s):  
Acoustic Emission ◽  
Sliding Friction ◽  
Monitoring And Control ◽  
Destructive Testing ◽  
Noise Sources ◽  
Processing Technologies ◽  
Time Frequency ◽  
On Line ◽  
Ae Signal ◽  
And Control

The acoustic emission method is one of few contemporary non-destructive testing techniques enabling continuous on-line health monitoring and control of tribological systems. However, the existence of multiple “pseudo”-acoustic emission (AE) and noise sources during friction, and their random occurrence poses serious challenges for researchers and practitioners when extracting “useful” information from the upcoming AE signal. These challenges and numerous uncertainties in signal classification prevent the unequivocal interpretation of results and hinder wider uptake of the AE technique despite its apparent advantages. Currently, the signal recording and processing technologies are booming, and new applications are born on this support. Specific tribology applications, therefore, call for developing new and tuning existing approaches to the online AE monitoring and analysis. In the present work, we critically analyze, compare and summarize the results of the application of several filtering techniques and AE signal classifiers in model tribological sliding friction systems allowing for the simulation of predominant wear mechanisms. Several effective schemes of AE data processing were identified through extensive comparative studies. Guidelines were provided for practical application, including the online monitoring and control of the systems with friction, characterizing the severity and timing of damage, on-line evaluation of wear as sliding contact tests and instrumented acceleration of tribological testing and cost reduction.

The Online Monitoring of Surface Quality Based on Time-Frequency Spectrum Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 564-568
Author(s):  
Chang Liu ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Lu Zhang
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Surface Quality ◽  
Machining Process ◽  
Work Piece ◽  
Time Frequency ◽  
Quality Of Work ◽  
On Line ◽  
Ae Signal

There is a high requirement on the surface quality of work-pieces made of Ni-based super-alloys due to the important application in aviation and aerospace fields, so it is particularly important to implement the on-line monitoring to the surface quality of work-piece in the machining process. The acoustic emission (AE) signal has the relatively superior signal/noise ratio and sensitivity during the process of nickel alloy. Through the analysis of AE signal’s characteristic which comes from the different condition of tool wear, it is an effective mean to evaluate the tool wear condition and monitor the surface quality of work-piece due to the usage of AE during the machining process. This paper indicate that it is simple and intuitive to achieve the on-line monitoring of surface quality which based on spectrum analysis of AE signal and proposed the method of on-line monitoring of the nickel alloy surface quality under different condition of tool wear based on AE time-frequency spectrum.

Wavelet Analysis of Elastic Wave for Wall Thinned Inconel 690 Tube

2007 ◽  
Vol 353-358 ◽  
pp. 2281-2284
Author(s):  
Seok Hwan Ahn ◽  
K.Y. Seong ◽  
Jin Wook Kim ◽  
Ki Woo Nam
Keyword(s):  
Wavelet Analysis ◽  
Structural Integrity ◽  
Bending Moment ◽  
Frequency Characteristics ◽  
Analysis Method ◽  
Simple Method ◽  
Time Frequency ◽  
Inconel 690 ◽  
Ae Signal ◽  
Ball Test

The bending moment test used by the specimens with partial and circumferential wall thinning was carried out to obtain the AE signal. The time-frequency analysis method for the investigation of the frequency characteristics of the AE signal was applied. The results of the wavelet analysis were compared with those of the bending moment test for the structural integrity of tube. The result of the frequency characteristics by applying wavelet analysis method and dropping ball test was presented similarly to those of bending moment test. It is considered that this simple method with combination of dropping ball test and wavelet analysis is very useful scheme for investigating the structural integrity.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

scholarly journals Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Classification Accuracy ◽  
Communication Systems ◽  
Mimo Systems ◽  
Decision Fusion ◽  
Frequency Characteristics ◽  
Modulation Classification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

The Real-Time Prediction of Surface Roughness Based on Genetic Wavelet Network

2010 ◽  
Vol 102-104 ◽  
pp. 610-614 ◽  
Author(s):  
Jun Chi ◽  
Lian Qing Chen
Keyword(s):  
Surface Roughness ◽  
Real Time ◽  
Back Propagation ◽  
Cutting Parameters ◽  
Wavelet Network ◽  
Back Propagation Algorithm ◽  
The Real ◽  
Roughness Model ◽  
On Line ◽  
Artificial Network

A methodology based on relax-type wavelet network was proposed for predicting surface roughness. After the influencing factors of roughness model were analyzed and the modified wavelet pack algorithm for signal filtering was discussed, the structure of artificial network for prediction was developed. The real-time forecast on line was achieved by the nonlinear mapping and learning mechanism in Elman algorithm based on the vibration acceleration and cutting parameters. The weights in network were optimized using genetic algorithm before back-propagation algorithm to reduce learning time.The validation of this methodology is carried out for turning aluminum and steel in the experiments and its prediction error is measured less than 3%.

Experiment on Surface Roughness and Acoustic Emission Signal in Infeed Period of Cylindrical Plunge Grinding

2013 ◽  
Vol 690-693 ◽  
pp. 2442-2445 ◽  
Author(s):  
Hao Lin Li ◽  
Hao Yang Cao ◽  
Chen Jiang
Keyword(s):  
Surface Roughness ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Feed Rate ◽  
Grinding Process ◽  
Experiment Research ◽  
Plunge Grinding ◽  
Emission Signal ◽  
Ae Signal ◽  
Ae Signals

This work presents an experiment research on Acoustic emission (AE) signal and the surface roughness of cylindrical plunge grinding with the different infeed time. The changed infeed time of grinding process is researched as an important parameter to compare AE signals and surface roughnesses with the different infeed time in the grinding process. The experiment results show the AE signal is increased by the increased feed rate. In the infeed period of the grinding process, the surface roughness is increased at first, and then is decreased.

The Optimization of Time-Frequency Control: Using Non-Autonomous Time-Delay Feedback Systems as Example

2016 ◽  
Author(s):  
Chi-Wei Kuo ◽  
C. Steve Suh
Keyword(s):  
Time Delay ◽  
Dynamic Instability ◽  
Frequency Control ◽  
Frequency Resolution ◽  
Control Technique ◽  
Time Step ◽  
Time Frequency ◽  
Control Scheme ◽  
Fxlms Algorithm ◽  
On Line

A novel time-frequency nonlinear scheme demonstrated to be feasible for the control of dynamic instability including bifurcation, non-autonomous time-delay feedback oscillators, and route-to-chaos in many nonlinear systems is applied to the control of a time-delayed system. The control scheme features wavelet adaptive filters for simultaneous time-frequency resolution. Specifically Discrete Wavelet transform (DWT) is used to address the nonstationary nature of a chaotic system. The concept of active noise control is also adopted. The scheme applied the filter-x least mean square (FXLMS) algorithm which promotes convergence speed and increases performance. In the time-frequency control scheme, the FXLMS algorithm is modified by adding an adaptive filter to identify the system in real-time in order to construct a wavelet-based time-frequency controller capable of parallel on-line modeling. The scheme of such a construct, which possesses joint time-frequency resolution and embodies on-line FXLMS, is able to control non-autonomous, nonstationary system responses. Although the controller design is shown to successfully moderate the dynamic instability of the time-delay feedback oscillator and unconditionally warrant a limit cycle, parameters are required to be optimized. In this paper, the setting of the control parameters such as control time step, sampling rate, wavelet filter vector, and step size are studied and optimized to control a time-delay feedback oscillators of a nonautonomous type. The time-delayed oscillators have been applied in a broad set of fields including sensor design, manufacturing, and machine dynamics, but they can be easily perturbed to exhibit complex dynamical responses even with a small perturbation from the time-delay feedback. These responses for the system have a very negative impact on the stability, and thus output quality. Through employingfrequency-time control technique, the time responses of the time-delay feedback system to external disturbances are properly mitigated and the frequency responses are also suppressed, thus rendering the controlled responses quasi-periodic.

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