scholarly journals An Efficient Approach for Identification of the Inlet Distortion of Engine Based on Acoustic Emission Technique

2020 ◽  
Vol 10 (22) ◽  
pp. 8240
Author(s):  
Jiaoyan Huang ◽  
Aiguo Xia ◽  
Shenao Zou ◽  
Cong Han ◽  
Guoan Yang
Keyword(s):  
Acoustic Emission ◽  
Health Management ◽  
Back Propagation ◽  
Vibration Monitoring ◽  
Characteristic Parameters ◽  
New Approach ◽  
Inlet Distortion ◽  
Monitoring Technique ◽  
Ae Signal ◽  
Intelligent Recognition

Effective and accurate diagnosis of engine health is key to ensuring the safe operation of engines. Inlet distortion is due to the flow or the pressure variations. In the paper, an acoustic emission (AE) online monitoring technique, which has a faster response time compared with the ordinary vibration monitoring technique, is used to study the inlet distortion of an engine. The results show that with the deterioration of the inlet distortion, the characteristic parameters of AE signals clearly evolve in three stages. Stage I: when the inlet distortion J ≤ 30%, the characteristic parameters of the AE signal increase as J increases and the amplitude saturates at J = 23%, faster than the other three parameters (the strength, the root mean square (RMS), and the average signal level (ASL)). Stage II: when the inlet distortion 30% < J ≤ 43.64%, all the parameters saturate with only slight fluctuations as J increases and the engine works in an unstable statue. Stage III: when the inlet distortion J > 43.64%, the engine is prone to surge. Furthermore, an intelligent recognition method of the engine inlet distortion based on a unit parameter entropy and the back propagation (BP) neural network is constructed. The recognition accuracy is as high as 97.5%, and this method provides a new approach for engine health management.

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.

Investigation of the Peculiarities of Deformation and Fracture of Metals Obtained on the Basis of 3D Printing with Registration of an Acoustic Emission Signal

2021 ◽  
Vol 1037 ◽  
pp. 71-76
Author(s):  
Maksim S. Anosov ◽  
Yury G. Kabaldin ◽  
Dmitrii A. Shatagin ◽  
Dmitry A. Ryabov ◽  
Pavel Kolchin
Keyword(s):  
Acoustic Emission ◽  
3D Printing ◽  
Test Temperature ◽  
Acoustic Emission Signal ◽  
Fracture Mechanisms ◽  
Characteristic Parameters ◽  
Diagnostic Features ◽  
Emission Signal ◽  
Deformation And Fracture ◽  
Ae Signal

The paper investigates the features of deformation and fracture of steels obtained using the technology of 3D printing by electric arc surfacing based on the registration of the acoustic emission signal. With a decrease in the test temperature of 07Cr25Ni13 steel, a decrease in the work expended in stretching the specimen is observed, both at the stage of elastic deformation and at the stage of strain hardening. It was found that the most informative characteristic parameters of the AE signal include: the pulse count rate N, the total count NΣ, and the AE signal entropy. With a decrease in the test temperature, there is a significant increase in the intensity of the AE signal, the total number of pulses at all stages of deformation and destruction of steel. The obtained regularities of changes in the characteristic parameters of the AE signal can be used as diagnostic features, both in assessing the stage of deformation and destruction of the material, and the structural state of the material. Fractographic studies have shown a significant decrease in the tough component of 08Mn2Si steel with a decrease in the test temperature. The fracture mechanisms of 07Cr25Ni13 steel change insignificantly with decreasing temperature, however, a significant decrease in the ductility of the metal is observed, as evidenced by a decrease in the size of ductile fracture cups.

Study on Acoustic Emission in the Grinding Process Automation

1999 ◽  
Author(s):  
Ming Chen ◽  
Bing-Yuan Xue
Keyword(s):  
Acoustic Emission ◽  
High Efficiency ◽  
Grinding Wheel ◽  
Grinding Process ◽  
Process Automation ◽  
Grinding Burn ◽  
Sensing System ◽  
Monitoring Technique ◽  
Ae Signal ◽  
Process Prediction

Abstract Comprehensive experiments have been conducted to investigate the monitoring technique for grinding process automation with acoustic emission (AE) signal. The AE signal generated during the grinding process is analyzed to determine its sensitivity to process. The detection of contact between the grinding wheel and workpiece and in-process prediction of grinding burn have been discussed in sequence. The results have been obtained as follows: (1) AE contact detector can save the non-machine time remarkably, thus high efficiency is available. (2) An effective intelligent sensing system has been developed and grinding burn can predicted. As mentioned above, AE technique has found wide applications in the grinding process automation.

Linear Location of Acoustic Emission Source Based on LS-SVR and NGA

2011 ◽  
Vol 80-81 ◽  
pp. 302-306
Author(s):  
Jing Tao Yu ◽  
Ming Li Ding ◽  
Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Acoustic Emission ◽  
Bp Neural Network ◽  
Location Problem ◽  
Support Vector ◽  
New Approach ◽  
Acoustic Emission Source ◽  
Ae Signal ◽  
Sharing Function ◽  
Fiber Materials

To solve the fatigue damage location problem of helicopter moving component, a new approach for linear location of acoustic emission (AE) source based on least squares support vector machine for regression (LS-SVR) and niche genetic algorithm (NGA) was proposed. Several time domain parameters of AE signal were taken as the inputs, and the linear coordinates of the breakpoints as the output. The sharing function based niche genetic algorithm is used to select the LS-SVR parameters automatically. The results of pencil lead break location experiment on specimen of carbon fiber materials indicate that the proposed approach can implement linear location of AE source effectively, and has better performance on convergence rate and location accuracy than RBF and BP neural network.

A New Approach to Automatic Detection of Life of Coated Tool Based on Acoustic Emission Measurement

1990 ◽  
Vol 112 (3) ◽  
pp. 212-218 ◽  
Author(s):  
T. Moriwaki ◽  
M. Tobito
Keyword(s):  
Acoustic Emission ◽  
Medium Carbon Steel ◽  
Emission Measurement ◽  
New Approach ◽  
Medium Carbon ◽  
Continuous Type ◽  
Coated Tool ◽  
Ae Signal ◽  
Characteristic Features ◽  
Acoustic Emission Measurement

Characteristic features of acoustic emission (AE) signals are analyzed and measured during turning of medium carbon steel with both coated and uncoated tools. It was found that the AE signal changes from burst-type to continuous-type as the coated tool is worn away and the ceramic coating is removed. The AE signal further changes to one with a large amplitude and variation as the tool approaches termination. A procedure is proposed to classify the AE signal into three categories based on the experimental results and to identify the condition of tool by AE signal measured employing the pattern recognition technique. Further cutting experiments proved that the state of wear and the life of the coated tool can be identified by the method proposed.

scholarly journals Challenges and Limitations in the Identification of Acoustic Emission Signature of Damage Mechanisms in Composites Materials

2018 ◽  
Vol 8 (8) ◽  
pp. 1267 ◽  
Author(s):  
Nathalie Godin ◽  
Pascal Reynaud ◽  
Gilbert Fantozzi
Keyword(s):  
Acoustic Emission ◽  
Health Management ◽  
Propagation Distance ◽  
Damage Mechanism ◽  
Surface Interactions ◽  
Stress Waves ◽  
Classification Algorithms ◽  
Damage Mechanisms ◽  
Composites Materials ◽  
Ae Signal

Acoustic emission is a part of structural health monitoring (SHM) and prognostic health management (PHM). This approach is mainly based on the activity rate and acoustic emission (AE) features, which are sensitive to the severity of the damage mechanism. A major issue in the use of AE technique is to associate each AE signal with a specific damage mechanism. This approach often uses classification algorithms to gather signals into classes as a function of parameters values measured on the signals. Each class is then linked to a specific damage mechanism. Nevertheless, each recorded signal depends on the source mechanism features but the stress waves resulting from the microstructural changes depend on the propagation and acquisition (attenuation, damping, surface interactions, sensor characteristics and coupling). There is no universal classification between several damage mechanisms. The aim of this study is the assessment of the influence of the type of sensors and of the propagation distance on the waveforms parameters and on signals clustering.

Intelligent recognition of semantic relationships based on antonymy

2020 ◽  
Vol 16 (3) ◽  
pp. 263-290
Author(s):  
Hui Guan ◽  
Chengzhen Jia ◽  
Hongji Yang
Keyword(s):  
Semantic Similarity ◽  
New Approach ◽  
Word Similarity ◽  
Semantic Relationships ◽  
Proposed Model ◽  
Path Distance ◽  
The Hierarchical Structure ◽  
Thinking Process ◽  
Similarity Measuring ◽  
Intelligent Recognition

Since computing semantic similarity tends to simulate the thinking process of humans, semantic dissimilarity must play a part in this process. In this paper, we present a new approach for semantic similarity measuring by taking consideration of dissimilarity into the process of computation. Specifically, the proposed measures explore the potential antonymy in the hierarchical structure of WordNet to represent the dissimilarity between concepts and then combine the dissimilarity with the results of existing methods to achieve semantic similarity results. The relation between parameters and the correlation value is discussed in detail. The proposed model is then applied to different text granularity levels to validate the correctness on similarity measurement. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has effective improvement to existing path-distance based methods on the word similarity level, in the meanwhile effectively correct existing sentence similarity method in some cases in Microsoft Research Paraphrase Corpus and SemEval-2014 date set.

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.

scholarly journals The duration-energy-size enigma for acoustic emission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Blai Casals ◽  
Karin A. Dahmen ◽  
Boyuan Gou ◽  
Spencer Rooke ◽  
Ekhard K. H. Salje
Keyword(s):  
Acoustic Emission ◽  
Mean Field Theory ◽  
Mean Field ◽  
Maximum Amplitude ◽  
Time Profile ◽  
Power Law Distribution ◽  
Model Predictions ◽  
Ae Signal ◽  
Strain Signal ◽  
Better Than

AbstractAcoustic emission (AE) measurements of avalanches in different systems, such as domain movements in ferroics or the collapse of voids in porous materials, cannot be compared with model predictions without a detailed analysis of the AE process. In particular, most AE experiments scale the avalanche energy E, maximum amplitude Amax and duration D as E ~ Amaxx and Amax ~ Dχ with x = 2 and a poorly defined power law distribution for the duration. In contrast, simple mean field theory (MFT) predicts that x = 3 and χ = 2. The disagreement is due to details of the AE measurements: the initial acoustic strain signal of an avalanche is modified by the propagation of the acoustic wave, which is then measured by the detector. We demonstrate, by simple model simulations, that typical avalanches follow the observed AE results with x = 2 and ‘half-moon’ shapes for the cross-correlation. Furthermore, the size S of an avalanche does not always scale as the square of the maximum AE avalanche amplitude Amax as predicted by MFT but scales linearly S ~ Amax. We propose that the AE rise time reflects the atomistic avalanche time profile better than the duration of the AE signal.

Diagnosis Research on Main Bearing Wear of Gasoline Engines in Mechanical Engineering

2013 ◽  
Vol 644 ◽  
pp. 304-307 ◽  
Author(s):  
Chang Shun Wang
Keyword(s):  
Gasoline Engine ◽  
Vibration Monitoring ◽  
Analysis Method ◽  
Characteristic Parameters ◽  
Main Bearing ◽  
Vibration Signals ◽  
Gasoline Engines ◽  
Test Spot ◽  
Monitoring Mechanism ◽  
Spectral Analysis Method

The different clearances of main bearing of previously designed on EQ6100 model gasoline engine is diagnosed by means of vibration monitoring mechanism. Breakdown signals of main test on different speed, clearance of main bearing, test spot and weather were analyzed by Spectral Analysis method and compared with normal and abnormal vibration signals. As a result, the characteristic parameters and the identifying methods of breakdown are given. In addition, the problems of fault detection are pointed out.

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