scholarly journals Time–Frequency Feature Extraction of Acoustic Emission Signals in Aluminum Alloy MIG Welding Process Based on SST and PCA

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 113988-113998 ◽  
Author(s):  
Kuanfang He ◽  
Xuejun Li
Keyword(s):  
Acoustic Emission ◽  
Feature Extraction ◽  
Aluminum Alloy ◽  
Welding Process ◽  
Mig Welding ◽  
Time Frequency ◽  
Frequency Feature

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.

Time-frequency feature extraction from frequency-modulated sounds in a population of auditory cortical neurons

2008 ◽  
Vol 29 (1) ◽  
pp. 36-40 ◽  
Author(s):  
Shunji Sugimoto ◽  
Yuta Suzuki ◽  
Hiroyuki Tanaka ◽  
Michinori Kubota ◽  
Junsei Horikawa
Keyword(s):  
Feature Extraction ◽  
Cortical Neurons ◽  
Time Frequency ◽  
Frequency Feature
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