scholarly journals The Use of Wavelet Transform To Evaluate The Sensitivity of Acoustic Emission Attributes To Variation of Cutting Parameters in Milling Aluminium Alloys

2021 ◽  
Author(s):  
Reza Asadi ◽  
Mohamad Javad Anahid ◽  
Hoda Heydarnia ◽  
Hedayeh Mehmanparast ◽  
Seyed Ali Niknam
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Monitoring And Control ◽  
Decomposition Numbers ◽  
Ae Signal ◽  
Ae Signals

Abstract Appropriate acquisition and assessment of the dominant acoustic emission (AE) signal attributes generated under various experimental cutting conditions may provide significant knowledge. Consequently, it enhances the efficiency in manufacturing process monitoring and control. However, according to the literature, a lack of information was noticed on the behavior of AE signal attributes under various cutting conditions. Considering that milling is among the most widely used machining operations, the aim of this investigation is to acquire adequate knowledge about interactions between cutting parameters and their direct and indirect effects on the obtained AE signals attributes from the milling process. In the course of this work, the effects of cutting conditions on the attributes calculated from wavelet transform (WT) of AE signals will be presented. WT signal processing was conducted with five models of mother wavelets, and appropriate decomposition numbers were deployed. The approximated signal attributes obtained from each decomposition were assessed. According to signal processing and statistical calculations, cutting speed, feed rate, and coating significantly impacted the variation of AE signal attributes. Also, the most sensitive AE signal attributes and decompositions were rms, std, entropy and energy, and 2nd and 6th decompositions, respectively. The outcome of this work can be integrated into advanced artificial intelligence (AI) approaches to implement real-time monitoring of manufacturing processes.

scholarly journals Wavelet application in acoustic emission signal detection of wire related events in pipeline

2021 ◽  
Author(s):  
Ran Wu
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Signal Detection ◽  
Time Scale ◽  
Inspection Time ◽  
Time Frequency ◽  
Pipeline Inspection ◽  
Ae Signal ◽  
Thesis Work

This thesis establishes an automatic classification program for the signal detection work in pipeline inspection. Time-scale analysis provides the basic methodology of this thesis work. The wavelet transform is implemented in the program for filtering out the majority of noise and detect needed signals. As a popular nondestructive test, acoustic emission (AE) testing has been widely used in many physical and engineering fields such as leak detection and pipeline inspection. Among those applied AE tests, a common problem is to extract the physical features of the ideal events, so as to detect similar signals. In acoustic signal processing, those features can be represented as joint time frequency distribution. However, classical signal processing methods only give global information on either time or frequency domain, while local information is lots. Although the short-time Fourier transform (STFT) is developed to analyze time and frequency details simultaneously, it can only achieve limited precision. Other time-frequency methods are also applied in AE signal processing, but they all have the problem of resolution and time consuming. Wavelet transform is a time-scale technique with adaptable precision, which makes better feature extraction and detail detection. This thesis is an application of wavelet transform in AE signal detection where various noise exists. The wavelet transform with Morelet wavelet as the mother wavelet provides the basis of the program for auto classification in this thesis work. Finally the program is tested with two industrial projects to verify the workability of wavelet transforms and the reliability of the developed auto classifiers.

Using Acoustic Emission and Vibration Detection to Identify the Rotor-Stator Rubbing

2007 ◽  
Author(s):  
Zhansheng Liu ◽  
Xiaowei Wang ◽  
Wei Dou
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Vibration Analysis ◽  
Turbine Rotor ◽  
Vibration Monitoring ◽  
Rotating Machine ◽  
Vibration Detection ◽  
Ae Signal ◽  
Many Sources ◽  
Ae Signals

Vibration monitoring of rotating machines is probably the most established diagnostic method. The application of acoustic emission (AE) for rotating machine fault diagnosis is gained as a complementary tool; however, limitations in the successful application of the AE technique have been partly due to the difficulty in processing, interpreting and classifying the acquired data. The experimental investigation reported in this paper is centred on the application of the AE technique for identifying the seal rubbing on the rotor rig. An experimental test rig was designed to simulate the 200MW gas turbine rotor shafts. On the rig different degrees rubbing-impact on the seal is performed. The AE transducer and the vibration acceleration transducer are set on the bearing block. Comparisons between AE and vibration analysis over a range of speed and different degrees rubbing-impact are presented. In fact there are so many sources of AE that the successful identification of rubbing-impact signal is very important. Account for the characteristics of acoustic emission signals the wavelet transform is employed to analyze the AE signal. The wavelet transform can decompose the AE signals in time and wavelet scale domains, and catch the differences in these waves. It enables to distinguish the rubbing-impact from other sources. It is concluded that AE offers earlier fault detection and improved identification capabilities than vibration analysis, allowing the user to monitor the rubbing-impact degrees of the rotor system, unachievable with vibration analysis.

scholarly journals Wavelet application in acoustic emission signal detection of wire related events in pipeline

2021 ◽  
Author(s):  
Ran Wu
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Signal Detection ◽  
Time Scale ◽  
Inspection Time ◽  
Time Frequency ◽  
Pipeline Inspection ◽  
Ae Signal ◽  
Thesis Work

This thesis establishes an automatic classification program for the signal detection work in pipeline inspection. Time-scale analysis provides the basic methodology of this thesis work. The wavelet transform is implemented in the program for filtering out the majority of noise and detect needed signals. As a popular nondestructive test, acoustic emission (AE) testing has been widely used in many physical and engineering fields such as leak detection and pipeline inspection. Among those applied AE tests, a common problem is to extract the physical features of the ideal events, so as to detect similar signals. In acoustic signal processing, those features can be represented as joint time frequency distribution. However, classical signal processing methods only give global information on either time or frequency domain, while local information is lots. Although the short-time Fourier transform (STFT) is developed to analyze time and frequency details simultaneously, it can only achieve limited precision. Other time-frequency methods are also applied in AE signal processing, but they all have the problem of resolution and time consuming. Wavelet transform is a time-scale technique with adaptable precision, which makes better feature extraction and detail detection. This thesis is an application of wavelet transform in AE signal detection where various noise exists. The wavelet transform with Morelet wavelet as the mother wavelet provides the basis of the program for auto classification in this thesis work. Finally the program is tested with two industrial projects to verify the workability of wavelet transforms and the reliability of the developed auto classifiers.

scholarly journals Denoising of Hydrogen Evolution Acoustic Emission Signal Based on Non-Decimated Stationary Wavelet Transform

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1460
Author(s):  
Zazilah May ◽  
Md Khorshed Alam ◽  
Noor A’in A. Rahman ◽  
Muhammad Shazwan Mahmud ◽  
Nazrul Anuar Nayan
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Carbon Steel ◽  
Hydrogen Evolution ◽  
Noise Removal ◽  
Threshold Function ◽  
Stationary Wavelet Transform ◽  
Reliable Technique ◽  
Ae Signal ◽  
Ae Signals

Monitoring the evolution of hydrogen gas on carbon steel pipe using acoustic emission (AE) signal can be a part of a reliable technique in the modern structural health-monitoring (SHM) field. However, the extracted AE signal is always mixed up with random extraneous noise depending on the nature of the service structure and experimental environment. The noisy AE signals often mislead the obtaining of the desired features from the signals for SHM and degrade the performance of the monitoring system. Therefore, there is a need for the signal denoising method to improve the quality of the extracted AE signals without degrading the original properties of the signals before using them for any knowledge discovery. This article proposes a non-decimated stationary wavelet transform (ND-SWT) method based on the variable soft threshold function for denoising hydrogen evolution AE signals. The proposed method filters various types of noises from the acquired AE signal and removes them efficiently without degrading the original properties. The hydrogen evolution experiments on carbon steel pipelines are carried out for AE data acquisition. Simulations on experimentally acquired AE signals and randomly generated synthetic signals with different levels of noise are performed by the ND-SWT method for noise removal. Results show that our proposed method can effectively eliminate Gaussian white noise as well as noise from the vibration and frictional activity and provide efficient noise removal solutions for SHM applications with minimum reconstruction error, to extract meaningful AE signals from the large-scale noisy AE signals during monitoring and inspection.

scholarly journals Classification of acoustic emission signals in wood damage and fracture process based on empirical mode decomposition, discrete wavelet transform methods, and selected features

2021 ◽  
Vol 67 (1) ◽  
Author(s):  
Meilin Zhang ◽  
Qinghui Zhang ◽  
Junqiu Li ◽  
Jiale Xu ◽  
Jiawen Zheng
Keyword(s):  
Acoustic Emission ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Damage And Fracture ◽  
Mode Decomposition ◽  
Ae Signal ◽  
Ae Signals

AbstractThe nondestructive testing technology of generated acoustic emission (AE) signals for wood is of great significance for the evaluation of internal damages of wood. To achieve more accurate and adaptive evaluation, an AE signals classification method combining the empirical mode decomposition (EMD), discrete wavelet transform (DWT), and linear discriminant analysis (LDA) classifier is proposed. Five features (entropy, crest factor, pulse factor, margin factor, waveform factor) are selected for classification because they are more sensitive to the uncertainty, complexity, and non-linearity of AE signals generated during wood fracture. The three-point bending load damage experiment was implemented on sample wood of beech and Pinus sylvestris to generate original AE signals. Evaluation indexes (precision, accuracy, recall, F1-score) were adopted to assess the classification model. The results show that the ensemble classification accuracies of two tree species reach 94.58% and 90.58%, respectively. Moreover, compared with the results of the original AE signal, the accuracy of the AE signal processed by the methods proposed is increased by 27.68%. It indicates that the EMD and DWT signal processing methods and selected features improve the classification accuracy, and this automatic classification model has good AE signal recognition performance.

scholarly journals Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

2021 ◽  
Vol 11 (15) ◽  
pp. 7045
Author(s):  
Ming-Chyuan Lu ◽  
Shean-Juinn Chiou ◽  
Bo-Si Kuo ◽  
Ming-Zong Chen
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Frequency Domain ◽  
Monitoring System ◽  
Welding Quality ◽  
Steel Sheets ◽  
Signal Features ◽  
Ae Signal ◽  
Laser Microwelding ◽  
Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

Effect of Cutting Parameters on Surface Roughness in Turning of Bone

2013 ◽  
Vol 845 ◽  
pp. 708-712 ◽  
Author(s):  
P.Y.M. Wibowo Ndaruhadi ◽  
S. Sharif ◽  
M.Y. Noordin ◽  
Denni Kurniawan
Keyword(s):  
Surface Roughness ◽  
Bone Tissue ◽  
Influencing Factor ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Cutting Conditions ◽  
Machining Process ◽  
Turning Operation ◽  
Cutting Direction

Surface roughness indicates the damage of the bone tissue due to bone machining process. Aiming at inducing the least damage, this study evaluates the effect of some cutting conditions to the surface roughness of machined bone. In the turning operation performed, the variables are cutting speed (26 and 45 m/min), feed (0.05 and 0.09 mm/rev), tool type (coated and uncoated), and cutting direction (longitudinal and transversal). It was found that feed did not significantly influence surface roughness. Among the influencing factor, the rank is tool type, cutting speed, and cutting direction.

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.

Research on Feature Extraction Method Based on Rub-Impact Acoustic Emission Signals of Mechanical Seal End Faces

2010 ◽  
Vol 36 ◽  
pp. 68-74
Author(s):  
Chuan Jun Liao ◽  
Shuang Fu Suo ◽  
Wei Feng Huang
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Mechanical Seal ◽  
Mechanical Seals ◽  
Feature Extraction Method ◽  
Different Types ◽  
Ae Signal ◽  
Feature Information ◽  
Ae Signals

Acoustic emission (AE) techniques are put forward to monitor rub-impacts between rotating rings and stationary rings of mechanical seals by this paper. By analyzing feature extraction methods of the typical rub-impact AE signal, the method combining of wavelet scalogram and power spectrum is found useful, and can used to attribute the feature information implicated in rub-impact AE signals of mechanical seal end faces. Both simulations and experimental research prove that the method is effective, and are used successfully to identify the typical features of different types of rub-impacts of mechanical seal end faces.

scholarly journals Potential of Empirical Mode Decomposition for Hilbert Demodulation of Acoustic Emission Signals in Gearbox Diagnostics

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Spectrum ◽  
Ae Signals

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals.

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