scholarly journals Study on Descriptors of Acoustic Emission Signals Generated by Partial Discharges under Laboratory Conditions and in On-Site Electrical Power Transformer

2016 ◽  
Vol 41 (2) ◽  
pp. 265-276 ◽  
Author(s):  
Michał Kunicki ◽  
Andrzej Cichoń ◽  
Sebastian Borucki
Keyword(s):  
Acoustic Emission ◽  
Power Transformer ◽  
Electrical Power ◽  
High Sensitivity ◽  
Laboratory Research ◽  
Time Frequency ◽  
Laboratory Conditions ◽  
Set Up ◽  
Point To Point ◽  
Ae Signals

Abstract An acoustic emission method (AE) is widespread and often applied for partial discharge (PD) diagnostics, mainly due to its ease of application as well as noninvasiveness and relatively high sensitivity. This paper presents comparative analysis of AE signals measurement results archived under laboratory conditions as well as on-site actual AE signals generated by inside PDs in electrical power transformer during its normal service. Three different PD model sources are applied for laboratory research: point to point, multipoint to plate and surface type. A typical measuring set up commonly used for on-site transformer PD diagnostics is provided for the laboratory tasks: piezoelectric joint transducer, preamplifier, amplifier and measuring PC interface. During the on-site research there are three measuring tracks applied simultaneously. Time domain, time-frequency domain and statistical tools are used for registered AE signals analysis. A number of descriptors are proposed as a result of the analysis. In the paper, at- tempt of AE signals descriptors, archived under laboratory condition application possibilities for on-site PD diagnostics of power transformers during normal service is made.

The influence of ultrasound signal parameters on sonoluminescence light intensity

2013 ◽  
Vol 62 (4) ◽  
pp. 605-612
Author(s):  
Marek Szmechta ◽  
Tomasz Boczar ◽  
Dariusz Zmarzły
Keyword(s):  
Acoustic Emission ◽  
Acoustic Waves ◽  
Power Transformer ◽  
Electrical Power ◽  
Partial Discharges ◽  
Research Project ◽  
Insulation Systems ◽  
Secondary Phenomenon ◽  
New Research ◽  
The Relationship

Abstract Topics of this article concern the study of the fundamental nature of the sonoluminescence phenomenon occurring in liquids. At the Institute of Electrical Power Engineering at Opole University of Technology the interest in that phenomenon known as secondary phenomenon of cavitation caused by ultrasound became the genesis of a research project concerning acoustic cavitation in mineral insulation oils in which a number of additional experiments performed in the laboratory aimed to determine the influence of a number of acoustic parameters on the process of the studied phenomenona. The main purpose of scientific research subject undertaken was to determine the relationship between the generation of partial discharges in high-voltage power transformer insulation systems, the issue of gas bubbles in transformer oils and the generated acoustic emission signals. It should be noted that currently in the standard approach, the phenomenon of generation of acoustic waves accompanying the occurrence of partial discharges is generally treated as a secondary phenomenon, but it can also be a source of many other related phenomena. Based on our review of the literature data on those referred subjects taken, it must be noted, that this problem has not been clearly resolved, and the description of the relationship between these phenomena is still an open question. This study doesn’t prove all in line with the objective of the study, but can be an inspiration for new research project in the future in this topic. Solution of this problem could be a step forward in the diagnostics of insulation systems for electrical power devices based on non-invasive acoustic emission method.

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 Intelligent Fault Diagnosis Method Using Acoustic Emission Signals for Bearings under Complex Working Conditions

2020 ◽  
Vol 10 (20) ◽  
pp. 7068
Author(s):  
Minh Tuan Pham ◽  
Jong-Myon Kim ◽  
Cheol Hong Kim
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Extraction Methods ◽  
High Accuracy ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Ae Signals

Recent convolutional neural network (CNN) models in image processing can be used as feature-extraction methods to achieve high accuracy as well as automatic processing in bearing fault diagnosis. The combination of deep learning methods with appropriate signal representation techniques has proven its efficiency compared with traditional algorithms. Vital electrical machines require a strict monitoring system, and the accuracy of these machines’ monitoring systems takes precedence over any other factors. In this paper, we propose a new method for diagnosing bearing faults under variable shaft speeds using acoustic emission (AE) signals. Our proposed method predicts not only bearing fault types but also the degradation level of bearings. In the proposed technique, AE signals acquired from bearings are represented by spectrograms to obtain as much information as possible in the time–frequency domain. Feature extraction and classification processes are performed by deep learning using EfficientNet and a stochastic line-search optimizer. According to our various experiments, the proposed method can provide high accuracy and robustness under noisy environments compared with existing AE-based bearing fault diagnosis methods.

scholarly journals Undervoltage Identification in Three Phase Induction Motor Using Low-Cost Piezoelectric Sensors and STFT Technique

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 72
Author(s):  
Leonardo Carvalho ◽  
Guilherme Lucas ◽  
Marco Rocha ◽  
Claudio Fraga ◽  
Andre Andreoli
Keyword(s):  
Acoustic Emission ◽  
Induction Motor ◽  
Large Scale ◽  
New Technologies ◽  
Low Cost ◽  
Industrial Applications ◽  
Piezoelectric Sensors ◽  
Time Frequency ◽  
Three Phase ◽  
Ae Signals

Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.

NON-DESTRUCTIVE EVALUATION AND STRENGTH CHARACTERISTICS OF CFRP WITH HEAT DAMAGE

2006 ◽  
Vol 20 (25n27) ◽  
pp. 4285-4290 ◽  
Author(s):  
JIN WOOK KIM ◽  
YOUNG UN KIM ◽  
CHANG KWON MOON ◽  
SEOK HWAN AHN ◽  
KI WOO NAM
Keyword(s):  
Acoustic Emission ◽  
Structural Integrity ◽  
Matrix Cracking ◽  
Fracture Mechanisms ◽  
Heat Damage ◽  
Time Frequency ◽  
Reinforced Plastic ◽  
Damage Process ◽  
Distinct Frequency ◽  
Ae Signals

In this study, the heat-damage process of a carbon fiber reinforced plastic (CFRP) under monotonic tensile loading was characterized by acoustic emission. Additionally, epoxy specimens and prepreg specimens were used to determine the characteristics of acoustic emission (AE) signals of epoxy and fiber, respectively. The AE characteristics of CFRP showed three types of distinct frequency regions. Time-frequency analysis methods were employed for the analysis of fracture mechanisms in CFRP such as matrix cracking, debonding and fiber fracture. To evaluate the cumulative counts of AE signals, it seems that the results can be applied usefully to guarantee structural integrity and/or to the survey of destruction of the structure with heat-damage, that was made to the composite materials.

scholarly journals Modal Acoustic Emission Investigation for Progressive Failure Monitoring in Thin Composite Plates under Tensile Test

2013 ◽  
Vol 558 ◽  
pp. 65-75
Author(s):  
Mohd Hafizi Zohari ◽  
Jayantha Ananda Epaarachchi ◽  
K.T. Lau
Keyword(s):  
Acoustic Emission ◽  
Single Channel ◽  
Quantitative Result ◽  
Composite Plates ◽  
Source Location ◽  
Theoretical Background ◽  
Emission Analysis ◽  
Time Frequency ◽  
Matrix Cracks ◽  
Ae Signals

Acoustic Emission (AE) is one of the popular non-destructive (NDT) techniques and its applications have increased subsequently for Structural Health Monitoring (SHM). During the past few decades, many successful research works have evidently shown remarkable capability of AE for early damage detection of composite materials. This paper investigated the application of single channel acoustic emission (AE) source location detection method, utilizing time-frequency analysis for thin composite plates. Besides, failure characterization using Modal Acoustic Emission (MAE) also presented. Modal analysis of AE signals or MAE can offers a better theoretical background for acoustic emission analysis; which is necessary to get more qualitative and quantitative result and therefore, increase the reliability of early failure characterization for thin composite plates. For this study, tensile tests were conducted on the glass fiber epoxy resin specimen with small notch; and four channels of acoustic emission system were used to acquire AE signals. The results revealed that in practical, single channel AE source location was difficult to be done. Also, the study has successfully showed that matrix cracks and fiber fracture produced AE signals which dominated by symmetric wave mode.

scholarly journals Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 57 ◽  
Author(s):  
Jing Tian ◽  
Lili Liu ◽  
Fengling Zhang ◽  
Yanting Ai ◽  
Rui Wang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Random Forest ◽  
High Sensitivity ◽  
Generalization Ability ◽  
Bearing Faults ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Ae Signal ◽  
Ae Signals

Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.

scholarly journals Identification of Acoustic Emission Signals Originating from the Core Magnetization of Power Oil Transformer

2016 ◽  
Vol 41 (4) ◽  
pp. 799-812 ◽  
Author(s):  
Aneta Olszewska ◽  
Franciszek Witos
Keyword(s):  
Power Transformer ◽  
Partial Discharges ◽  
Time Frequency ◽  
The Core ◽  
Frequency Domains ◽  
Extended Frequency Range ◽  
Extended Frequency ◽  
Side Walls ◽  
Ae Signals

Abstract In this paper, the properties of AE signals originating from phenomena occurring during magnetization of ferromagnetic materials which are used to construct power transformer cores are presented. The AE signals in a selected power oil transformer were recorded and analyzed. The analysis included, i.e., time, frequency, and time-frequency analyses, calculations of amplitude distributions of the signals and defined AE descriptors, determination of the descriptor map on the side walls of transformers, as well as a detailed analysis of selected part of the signals. The maps of descriptors were analyzed in the frequency bands of 20–70 kHz, 70–100 kHz, and 100–200 kHz. The analysis of the properties of the signals was performed in time and frequency domains. Based on the analysis, there were identified the AE signals originating from the phenomena occurring during the core magnetization of a power oil transformer. To identify those phenomena, the maps of the ADC descriptor calculated in the band of 20–70 kHz when selecting the measurement points in which there were no AE sources from partial discharges were used. An analysis of magnetoacoustic emission signals in the bands of 70–100 kHz and 100–200 kHz was also performed. The analysis of the signal properties in such an extended frequency range allowed determining the properties of the magnetoacoustic signals coming from core sheets of power oil transformers.

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