Signal feature extraction base on fractal dimensions of time-frequency domain

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

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Event Discrimination Method for Distributed Optical Fiber Intrusion Sensing System Based on Integrated Time/Frequency Domain Feature Extraction

Acta Optica Sinica ◽

10.3788/aos201939.0628002 ◽

2019 ◽

Vol 39 (6) ◽

pp. 0628002 ◽

Cited By ~ 1

Author(s):

彭宽 Kuan Peng ◽

冯诚 Cheng Feng ◽

王森懋 Senmao Wang ◽

艾凡 Fan Ai ◽

李豪 Hao Li ◽

...

Keyword(s):

Feature Extraction ◽

Optical Fiber ◽

Frequency Domain ◽

Time Frequency ◽

Sensing System ◽

Distributed Optical Fiber ◽

Discrimination Method

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A Novel Local Time-Frequency Domain Feature Extraction Method for Tool Condition Monitoring Using S-Transform and Genetic Algorithm

IFAC Proceedings Volumes ◽

10.3182/20140824-6-za-1003.02515 ◽

2014 ◽

Vol 47 (3) ◽

pp. 3516-3521 ◽

Cited By ~ 4

Author(s):

Javad Soltani Rad ◽

Youmin Zhang ◽

Chevy Chen

Keyword(s):

Genetic Algorithm ◽

Feature Extraction ◽

Frequency Domain ◽

Condition Monitoring ◽

Extraction Method ◽

Tool Condition Monitoring ◽

Feature Extraction Method ◽

Time Frequency ◽

Tool Condition ◽

S Transform

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OPTIMAL ACCURACY PERFORMANCE IN MUSIC-BASED EEG SIGNAL USING MATTHEW CORRELATION COEFFICIENT ADVANCED (MCCA)

Jurnal Teknologi ◽

10.11113/jurnalteknologi.v83.16750 ◽

2021 ◽

Vol 83 (6) ◽

pp. 53-61

Author(s):

Mahfuzah Mustafa ◽

Zarith Liyana Zahari ◽

Rafiuddin Abdubrani

Keyword(s):

Feature Extraction ◽

Frequency Domain ◽

Correlation Coefficient ◽

Frequency Domain Analysis ◽

Index Formula ◽

Eeg Signal ◽

Time Frequency ◽

Matthew Correlation Coefficient ◽

Accuracy Performance ◽

Arousal And Valence

The connection between music and human are very synonyms because music could reduce stress. The state of stress could be measured using EEG signal, an electroencephalogram (EEG) measurement which contains an arousal and valence index value. In previous studies, it is found that the Matthew Correlation Coefficient (MCC) performance accuracy is of 85±5%. The arousal indicates strong emotion, and valence indicates positive and negative degree of emotion. Arousal and valence values could be used to measure the accuracy performance. This research focuses on the enhance MCC parameter equation based on arousal and valence values to perform the maximum accuracy percentage in the frequency domain and time-frequency domain analysis. Twenty-one features were used to improve the significance of feature extraction results and the investigated arousal and valence value. The substantial feature extraction involved alpha, beta, delta and theta frequency bands in measuring the arousal and valence index formula. Based on the results, the arousal and valance index is accepted to be applied as parameters in the MCC equations. However, in certain cases, the improvement of the MCC parameter is required to achieve a high accuracy percentage and this research proposed Matthew correlation coefficient advanced (MCCA) in order to improve the performance result by using a six sigma method. In conclusion, the MCCA equation is established to enhance the existing MCC parameter to improve the accuracy percentage up to 99.9% for the arousal and valence index.

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Evaluation of Feature Extraction and Classification for Lower Limb Motion Based on sEMG Signal

Entropy ◽

10.3390/e22080852 ◽

2020 ◽

Vol 22 (8) ◽

pp. 852 ◽

Cited By ~ 2

Author(s):

Pengjie Qin ◽

Xin Shi

Keyword(s):

Feature Extraction ◽

Frequency Domain ◽

Classification Accuracy ◽

Evaluation Method ◽

Lower Limbs ◽

Thigh Muscle ◽

Gaussian Kernel ◽

Classification Methods ◽

Linear Discriminant ◽

Time Frequency

The real-time and accuracy of motion classification plays an essential role for the elderly or frail people in daily activities. This study aims to determine the optimal feature extraction and classification method for the activities of daily living (ADL). In the experiment, we collected surface electromyography (sEMG) signals from thigh semitendinosus, lateral thigh muscle, and calf gastrocnemius of the lower limbs to classify horizontal walking, crossing obstacles, standing up, going down the stairs, and going up the stairs. Firstly, we analyzed 11 feature extraction methods, including time domain, frequency domain, time-frequency domain, and entropy. Additionally, a feature evaluation method was proposed, and the separability of 11 feature extraction algorithms was calculated. Then, combined with 11 feature algorithms, the classification accuracy and time of 55 classification methods were calculated. The results showed that the Gaussian Kernel Linear Discriminant Analysis (GK-LDA) with WAMP had the highest classification accuracy rate (96%), and the calculation time was below 80 ms. In this paper, the quantitative comparative analysis of feature extraction and classification methods was a benefit to the application for the wearable sEMG sensor system in ADL.

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Big Data Quantitative Risk Analysis Method for Machine Health Indicator Prediction

10.14293/s2199-1006.1.sor-.pp55dcw.v1 ◽

2021 ◽

Author(s):

George Jordan ◽

Allan Brimicombe ◽

Yang Li

Keyword(s):

Feature Extraction ◽

Data Compression ◽

Frequency Domain ◽

Health Indicators ◽

Fast Fourier Transformation ◽

Time Frequency ◽

Vibration Data ◽

Bearing Vibration ◽

Machine Health ◽

Health Deterioration

Various data-driven methods have been applied to predict machine health indicators especially in the field of prognostics. Machine health indicators reveal the condition of equipment and/or its components including bearings by monitoring their operation data such as frequency vibration. To aid the prediction of the machine health indicators, this study applies the BDQRA method to monitor the health of bearings as a component of the machine. The BDQRA method involves applying data compression techniques like feature extraction to the bearing vibration data, to extract the most important features like time-domain, frequency domain, and time–frequency domain features. Due to the complexity of the feature extraction process, this study proposes fast Fourier transformation for the data compression. This is followed by obtaining a time series profile of the bearing vibration data to analyse the health status of component bearing. It the uses change-point analysis to predict the period at which the bearing health deterioration is imminent. Since the bearing health deterioration could be due to the independent operation of a component bearing or through communication between the component bearing and other components (or bearings) within the process machinery, the method also applies the principle of interaction effect to investigate the contributions from the other components of the machinery to the health deterioration of the component bearing detected. The accuracy of the prediction of the point of imminent health deterioration of the component bearing is investigated by comparing the outcome of the BDQRA method with the outcome of other methods published in literature which have been applied to the dataset used in this study. The findings reveal the BDQRA method have comparative advantages to the methods used in the related studies.

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