P-Wave Onset Detection Based on the Spectrograms of the AE Signals

2011 ◽  
Vol 250-253 ◽  
pp. 3807-3810 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Acoustic Emissions ◽  
P Wave ◽  
Onset Detection ◽  
Arrival Times ◽  
Time Frequency ◽  
Frequency Representation ◽  
Reasonable Result ◽  
Adaptive Thresholds ◽  
Ae Signal ◽  
Ae Signals

Determining the onset of acoustic emissions (AE) signal is very time consuming if the onset is picked manually, so it is important to read the arrival times of P phase automatically. Automatic onset detection and picking algorithm has been proposed by applying the spectro-ratio on time–frequency sub-band. Powers of frequency sub-bands are determined by spectrogram as a time–frequency representation. Adaptive thresholds are calculated for one of these sub-bands to check if there is a P-wave arrival in the segment or not. To verify this check another test is done using the spectro-ratio. The application of this algorithm on the AE signals from the pencil-breaking test shows a reasonable result.

An Effective Way to Validate Signal Arrival Time in AE Structural Monitoring

2010 ◽  
Vol 163-167 ◽  
pp. 2471-2476 ◽  
Author(s):  
Jie Xu
Keyword(s):  
Acoustic Emissions ◽  
Information Criterion ◽  
Practical Method ◽  
P Wave ◽  
Apparent Velocity ◽  
Arrival Times ◽  
Data Errors ◽  
Degree Of Certainty ◽  
Ae Signal ◽  
Two Parameters

A practical method, based on the Akaike Information Criterion (AIC), is used to detect automatically the first P-wave arrival times during Acoustic Emissions (AE) monitoring. This method is used to increase the accuracy of detection procedure and to reject the data errors automatically. Two parameters, the quantification of the degree of certainty and the apparent velocity between observation sensors, were proposed to check the validity of AE events and to neglect errors. Both the original AIC-picker and improved AIC-picker algorithms are applied to AE signal processing. It can be shown that the improved AIC-picker is a reliable tool for automatic onset detection of AE signals.

Novel Method for Detecting Weak AE Signals Based on the Similarity of Time-Frequency Spectra

Geophysics ◽  
2021 ◽  
pp. 1-62
Author(s):  
Wencheng Yang ◽  
Xiao Li ◽  
Yibo Wang ◽  
Yue Zheng ◽  
Peng Guo
Keyword(s):  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Critical Role ◽  
Real Data ◽  
Monitoring Method ◽  
Frequency Spectra ◽  
Arrival Times ◽  
Time Frequency ◽  
Fracturing Process ◽  
Ae Signals

As a key monitoring method, the acoustic emission (AE) technique has played a critical role in characterizing the fracturing process of laboratory rock mechanics experiments. However, this method is limited by low signal-to-noise ratio (SNR) because of a large amount of noise in the measurement and environment and inaccurate AE location. Furthermore, it is difficult to distinguish two or more hits because their arrival times are very close when AE signals are mixed with the strong background noise. Thus, we propose a new method for detecting weak AE signals using the mathematical morphology character correlation of the time-frequency spectrum. The character in all hits of an AE event can be extracted from time-frequency spectra based on the theory of mathematical morphology. Through synthetic and real data experiments, we determined that this method accurately identifies weak AE signals. Compared with conventional methods, the proposed approach can detect AE signals with a lower SNR.

scholarly journals Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning

Lubricants ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 29 ◽  
Author(s):  
Noushin Mokhtari ◽  
Jonathan Gerald Pelham ◽  
Sebastian Nowoisky ◽  
José-Luis Bote-Garcia ◽  
Clemens Gühmann
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Friction And Wear ◽  
Journal Bearing ◽  
Journal Bearings ◽  
Wear Volume ◽  
Time Frequency ◽  
Ae Signal ◽  
Ae Signals

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.

scholarly journals A prediction framework with time-frequency localization feature for detecting the onset of seismic events

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250008
Author(s):  
Kanchan Aggarwal ◽  
Siddhartha Mukhopadhya ◽  
Arun K. Tangirala
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Real Time ◽  
Detection Rate ◽  
Early Warning Systems ◽  
P Wave ◽  
Onset Detection ◽  
Time Frequency ◽  
Low Snr ◽  
Time Frequency Localization

Onset detection of P-wave in seismic signals is of vital importance to seismologists because it is not only crucial to the development of early warning systems but it also aids in estimating the seismic source parameters. All the existing P-wave onset detection methods are based on a combination of statistical signal processing and time-series modeling ideas. However, these methods do not adequately accommodate some advanced ideas that exist in fault detection literature, especially those based on predictive analytics. When combined with a time-frequency (t-f) / temporal-spectral localization method, the effectiveness of such methods is enhanced significantly. This work proposes a novel real-time automatic P-wave detector and picker in the prediction framework with a time-frequency localization feature. The proposed approach brings a diverse set of capabilities in accurately detecting the P-wave onset, especially in low signal-to-noise ratio (SNR) conditions that all the existing methods fail to attain. The core idea is to monitor the difference in squared magnitudes of one-step-ahead predictions and measurements in the time-frequency bands with a statistically determined threshold. The proposed framework essentially accommodates any suitable prediction methodology and time-frequency transformation. We demonstrate the proposed framework by deploying auto-regressive integrated moving average (ARIMA) models for predictions and the well-known maximal overlap discrete wavelet packet transform (MODWPT) for the t-f projection of measurements. The ability and efficacy of the proposed method, especially in detecting P-waves embedded in low SNR measurements, is illustrated on a synthetic data set and 200 real-time data sets spanning four different geographical regions. A comparison with three prominently used detectors, namely, STA/LTA, AIC, and DWT-AIC, shows improved detection rate for low SNR events, better accuracy of detection and picking, decreased false alarm rate, and robustness to outliers in data. Specifically, the proposed method yields a detection rate of 89% and a false alarm rate of 11.11%, which are significantly better than those of existing methods.

Extended finite element method for analysis of the acoustic emission response by a crack under moving heat source

2018 ◽  
Vol 35 (3) ◽  
pp. 1414-1443 ◽  
Author(s):  
Kuanfang He ◽  
Wei Lu ◽  
Xiangnan Liu ◽  
Siwen Xiao ◽  
Xuejun Li
Keyword(s):  
Heat Source ◽  
Moving Heat Source ◽  
Heating Effect ◽  
Extended Finite Element ◽  
Content Type ◽  
Time Frequency ◽  
Signal Characteristics ◽  
Ae Signal ◽  
Thermoelastic Plate ◽  
Ae Signals

Purpose This paper aims to study acoustic emission (AE) propagation characteristics by a crack under a moving heat source, which mainly provides theoretical basis and method for the actual crack detection during welding process. Design/methodology/approach The paper studied the AE characteristics in welding using thermoelastic theory, which investigates the dynamical displacement field caused by a crack and the welding heating effect. In the calculation model, the crack initiation and extension are represented by moment tensor as the AE source, and the welding heat source is the Gauss heat flux distribution. The extended finite element method (XFEM) is implemented to calculate and solve the AE response of a thermoelastic plate with a crack during the welding heating effect. The wavelet transform is applied to the time–frequency analysis of the AE signals. Findings The paper provides insights about the changing rule of the acoustic radiation patterns influenced by the heating effect of the moving heat source and the AE signal characteristics in thermoelastic plate by different crack lengths and depths. It reveals that the time–frequency characteristics of the AE signals from the simulation are in good agreement with the theoretical ones. The energy ratio of the antisymmetric mode A0 to symmetric mode S0 is a valuable quantitative inductor to estimate the crack depth with a certain regularity. Research limitations/implications This paper mainly discusses the application of XFEM to calculate and analyze thermoelastic problems, and has presented few cases based on a specified configuration. Further work will focus on the calculation and analysis under different plate configurations and conditions, which is to obtain more interesting and general conclusions for guiding practice. Originality/value The paper is a successful application of XFEM to solve the problem of AE response of a crack in the dynamic welding inhomogeneous heating effect. The paper provides an effective way to obtain the AE signal characteristics in monitoring the welding crack.

Gas Turbine Condition Monitoring Using Acoustic Emission Signals

2019 ◽  
Vol 2 (3) ◽  
Author(s):  
S. Shahkar ◽  
K. Khorasani
Keyword(s):  
Acoustic Emission ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Gas Turbines ◽  
Acoustic Emissions ◽  
Time Instant ◽  
Rotary Machinery ◽  
Ae Signal ◽  
Reliable Classification ◽  
Ae Signals

Acoustic emission (AE) signals are recognized as complementary measures for detecting incipient faults and condition monitoring in rotary machinery due to their containment of sources of potential fault energy. However, determining the potential sources of faults cannot be easily realized due to the non-stationarity of AE signals. Available techniques that are capable of evoking instantaneous characteristics of a particular AE signal cannot optimally perform in a sense that there is no guarantee that these characteristics (hereinafter referred to as the “features”) remain constant when another AE signal is obtained from the system, albeit operating under the same machine condition at a different time instant. This paper provides a theoretical framework for developing a highly reliable classification and detection methodology for gas turbine condition monitoring based on AE signals. Mathematical results obtained in this paper are evaluated and validated by using actual gas turbines that are operating in power generating plants, to demonstrate the practicality and simplicity of our methodologies. Emphasis is given to acoustic emissions of similar brand and sized gas turbine turbomachinery under different health conditions and/or aging characteristics.

scholarly journals Combination of Principal Component Analysis and Time-Frequency Representation for P-Wave Arrival Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Jacek Wodecki ◽  
Justyna Hebda-Sobkowicz ◽  
Adam Mirek ◽  
Radosław Zimroz ◽  
Agnieszka Wyłomańska
Keyword(s):  
Principal Component Analysis ◽  
High Efficiency ◽  
Onset Time ◽  
Principal Component ◽  
Component Analysis ◽  
P Wave ◽  
Time Frequency ◽  
Frequency Representation ◽  
Significant Difference ◽  
Wave Arrival

Seismic events are phenomena which commonly occur in the mining industry. Due to their dangerous character, such information as the energy of the potential event, the location of hazardous regions with higher seismic activity is considered valuable. However, the acquisition of this information is almost impossible without the ability to detect the onset time of the seismic event. The main objectives of algorithms in finding P-wave are high accuracy, reasonable time of operation, and automatic detection of wave arrival. In this paper, an innovative method which incorporates principal component analysis (PCA) with time-frequency representation of the signal is proposed. Due to the significant difference between the spectra of recorded seismic wave and pure noise which precedes the event, time-frequency representation allows for better accuracy of signal change detection. However, with an additional domain, the complexity rises. Thus, the incorporation of PCA (which is known for high efficiency in lowering data dimensions while maintaining original information) seems to be recommended. In order to show the feasibility of the method, it will be tested on real data originating from monitoring system used in underground mine.

The Method of Fault Feature Extraction from Acoustic Emission Signals Using Wigner-Ville Distribution

2011 ◽  
Vol 216 ◽  
pp. 732-737 ◽  
Author(s):  
G.F. Bin ◽  
C.J. Liao ◽  
Xue Jun Li
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Energy ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Fault Features ◽  
Very High Energy ◽  
Ae Signal ◽  
Experimental Data Analysis ◽  
Ae Signals

Wigner-Ville distribution (WVD) has the characteristics of very high-energy accumulation and excellent time-frequency resolution. It is a good way to extract fault feature of acoustic emission (AE) signals due to mechanical component broken. The characteristics of typical AE signals initiated by damages are analyzed. Based on the extracting principle of AE signals from damaged components, the WVD analysis method of AE signal is developed. WVD method is employed to the fault diagnosis of rolling bearings with AE technique. The fault features reading from experimental data analysis are clear, accurate and intuitionistic, meantime, the validity and accuracy of WVD method proposed are nice from the experimental results. Therefore, WVD method is useful for condition monitoring and fault diagnosis in conjunction with AE technique.

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