scholarly journals Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning

2019 ◽  
Vol 21 (3) ◽  
pp. 403-410 ◽  
Author(s):  
Xueyi Li ◽  
Jialin Li ◽  
David He ◽  
Yongzhi Qu
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Acoustic Emission Signal ◽  
Emission Signal

Acoustic Emission Signal Classification Using Feature Analysis and Deep Learning Neural Network

2020 ◽  
pp. 2150030
Author(s):  
Jian-Da Wu ◽  
Yu-Han Wong ◽  
Wen-Jun Luo ◽  
Kai-Chao Yao
Keyword(s):  
Acoustic Emission ◽  
Deep Learning ◽  
Frequency Domain ◽  
Acoustic Emission Signal ◽  
Signal Classification ◽  
Learning Method ◽  
Identification Rate ◽  
Emission Signal ◽  
Identification Time ◽  
The Impact

With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

Fault Diagnosis Based on Acoustic Emission Signal for Low Speed Rolling Element Bearing

2011 ◽  
Vol 199-200 ◽  
pp. 1020-1023 ◽  
Author(s):  
Hua Qing Wang ◽  
Yong Wei Guo ◽  
Jian Feng Yang ◽  
Liu Yang Song ◽  
Jia Pan ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Acoustic Emission Signal ◽  
Rotating Machinery ◽  
Rolling Element Bearing ◽  
Low Speed ◽  
Rotating Speed ◽  
Emission Signal ◽  
Rolling Element ◽  
Element Bearing

The fault of a bearing may cause the breakdown of a rotating machine, leading to serious consequences. A rolling element bearing is an important part of, and is widely used in rotating machinery. Therefore, fault diagnosis of rolling bearings is important for guaranteeing production efficiency and plant safety. Although many studies have been carried out with the goal of achieving fault diagnosis of a bearing, most of these works were studied for rotating machinery with a high rotating speed rather than with a low rotating speed. Fault diagnosis for bearings under a low rotating speed, is more difficult than under a high rotating speed. Because bearing faults signal is very weak under a low rotating speed. This work acquires vibration and acoustic emission signals from the rolling bearing under low speed respectively, and analyzes the both kinds of signals in time domain and frequency domain for diagnosing the typical bearing faults contrastively. This paper also discussed the advantages using the acoustic emission signal for fault diagnosis of rolling speed bearing. From the results of analysis and experiment we can find the effectiveness of acoustic emission signal is better than vibration signal for fault diagnosis of a bearing under the low speed.

Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review

2014 ◽  
Vol 69 (2) ◽  
Author(s):  
Yasir Hassan Ali ◽  
Roslan Abd Rahman ◽  
Raja Ishak Raja Hamzah
Keyword(s):  
Artificial Intelligence ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Acoustic Emission Signal ◽  
Signal Analysis ◽  
Machine Condition ◽  
Artificial Intelligence Techniques ◽  
Emission Signal ◽  
Machine Condition Monitoring

Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.

Research on Feature Extraction Experiment for Acoustic Emission Signal of Rotor Crack Fault

2010 ◽  
Vol 34-35 ◽  
pp. 1005-1009 ◽  
Author(s):  
Kuan Fang He ◽  
Xue Jun Li ◽  
X.C. Li
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Acoustic Emission Signal ◽  
Wavelet Packet ◽  
Fault Simulation ◽  
Early Period ◽  
Test Bed ◽  
Simulation Test ◽  
Emission Signal ◽  
Extraction Experiment

The acoustic emission extraction experiment of rotor crack fault has done on the rotor comprehensive fault simulation test-bed. Characteristics of acoustic emission signal of different crack rotors in various depth and conditions are analyzed. The noise-disturbing problems and the noise-eliminating methods of the acoustic emission signal were researched in the paper, and the comparison has been done with the vibration method of crack fault diagnosis by the experiment. The advantages of acoustic emission technique has been highlighted in the early period crack fault diagnosis. The wavelet packet technique was applied to obtain the characteristics of acoustic emission signal of the rotor crack propagation. the diagnosis results are shown to be quite clear, reliable and accurate.

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