Feature extraction based on the fractional Fourier transform for vibration signals with application to measuring the load of a tumbling mill

2019 ◽  
Vol 84 ◽  
pp. 238-246 ◽  
Author(s):  
Jianquan Shi ◽  
Gangquan Si ◽  
Shuiwang Li ◽  
Babajide Oresanya ◽  
Yanbin Zhang
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Vibration Signals ◽  
Tumbling Mill

A Novel Artificial Intelligence Technique for Analysis of Real-Time Electro-Cardiogram Signal for the Prediction of Early Cardiac Ailment Onset

2020 ◽  
pp. 42-66
Author(s):  
Dinesh Bhatia ◽  
Animesh Mishra
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Mapping Technique ◽  
Self Organizing Maps ◽  
Map Generation ◽  
Unsupervised Training ◽  
Cardio Vascular ◽  
Short Time

The role of ECG analysis in the diagnosis of cardio-vascular ailments has been significant in recent times. Although effective, the present computational algorithms lack accuracy, and no technique till date is capable of predicting the onset of a CVD condition with precision. In this chapter, the authors attempt to formulate a novel mapping technique based on feature extraction using fractional Fourier transform (FrFT) and map generation using self-organizing maps (SOM). FrFT feature extraction from the ECG data has been performed in a manner reminiscent of short time Fourier transform (STFT). Results show capability to generate maps from the isolated ECG wavetrains with better prediction capability to ascertain the onset of CVDs, which is not possible using conventional algorithms. Promising results provide the ability to visualize the data in a time evolution manner with the help of maps and histograms to predict onset of different CVD conditions and the ability to generate the required output with unsupervised training helping in greater generalization than previous reported techniques.

scholarly journals Feature Extraction of Impulse Faults for Vibration Signals Based on Sparse Non-Negative Tensor Factorization

2019 ◽  
Vol 9 (18) ◽  
pp. 3642
Author(s):  
Lin Liang ◽  
Haobin Wen ◽  
Fei Liu ◽  
Guang Li ◽  
Maolin Li
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Mechanical Equipment ◽  
Tensor Factorization ◽  
Short Time Fourier Transform ◽  
Vibration Signals ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Value Decomposition ◽  
Short Time

The incipient damages of mechanical equipment excite weak impulse vibration, which is hidden, almost unobservable, in the collected signal, making fault detection and failure prevention at the inchoate stage rather challenging. Traditional feature extraction techniques, such as bandpass filtering and time-frequency analysis, are suitable for matrix processing but challenged by the higher-order data. To tackle these problems, a novel method of impulse feature extraction for vibration signals, based on sparse non-negative tensor factorization is presented in this paper. Primarily, the phase space reconstruction and the short time Fourier transform are successively employed to convert the original signal into time-frequency distributions, which are further arranged into a three-way tensor to obtain a time-frequency multi-aspect array. The tensor is decomposed by sparse non-negative tensor factorization via hierarchical alternating least squares algorithm, after which the latent components are reconstructed from the factors by the inverse short time Fourier transform and eventually help extract the impulse feature through envelope analysis. For performance verification, the experimental analysis on the bearing datasets and the swashplate piston pump has confirmed the effectiveness of the proposed method. Comparisons to the traditional methods, including maximum correlated kurtosis deconvolution, singular value decomposition, and maximum spectrum kurtosis, also suggest its better performance of feature extraction.

Gear fault feature extraction based on fractional Fourier transform

2016 ◽  
pp. 931-936
Author(s):  
Hongfang Chen ◽  
Yanqiang Sun ◽  
Zhaoyao Shi ◽  
Jiachun Lin ◽  
Zaihua Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Gear Fault ◽  
Fault Feature Extraction

scholarly journals A New Time–Frequency Feature Extraction Method for Action Detection on Artificial Knee by Fractional Fourier Transform

Micromachines ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 333 ◽  
Author(s):  
Tianrun Wang ◽  
Ning Liu ◽  
Zhong Su ◽  
Chao Li
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Extraction Method ◽  
Fractional Fourier Transform ◽  
Feature Vector ◽  
Action Detection ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Frequency Feature ◽  
New Time

With the aim of designing an action detection method on artificial knee, a new time–frequency feature extraction method was proposed. The inertial data were extracted periodically using the microelectromechanical systems (MEMS) inertial measurement unit (IMU) on the prosthesis, and the features were extracted from the inertial data after fractional Fourier transform (FRFT). Then, a feature vector composed of eight features was constructed. The transformation results of these features after FRFT with different orders were analyzed, and the dimensions of the feature vector were reduced. The classification effects of different features and different orders are analyzed, according to which order and feature of each sub-classifier were designed. Finally, according to the experiment with the prototype, the method proposed above can reduce the requirements of hardware calculation and has a better classification effect. The accuracies of each sub-classifier are 95.05%, 95.38%, 91.43%, and 89.39%, respectively; the precisions are 78.43%, 98.36%, 98.36%, and 93.41%, respectively; and the recalls are 100%, 93.26%, 86.96%, and 86.68%, respectively.

Experimental Evaluation of a Unified Time-Scale-Frequency Technique for Bearing Defect Feature Extraction

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Changting Wang
Keyword(s):  
Feature Extraction ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Discrete Wavelet ◽  
Vibration Signals ◽  
Bearing Defect ◽  
Bearing Condition Monitoring ◽  
The Fourier Transform ◽  
Good Signal ◽  
Health Diagnosis

A systematic experimental study is presented in this paper on evaluating the effectiveness of a unified, multidomain algorithm for defect feature extraction in bearing condition monitoring and health diagnosis. The algorithm decomposes vibration signals measured on bearings by discrete wavelet transform and subsequently performs the Fourier transform on the wavelet coefficients. The effectiveness of such a unified technique is demonstrated through experimental case studies, which confirmed its advantage over the wavelet or Fourier transform techniques employed alone. Also, the unified technique has shown to be computationally more efficient than the enveloping technique based on continuous wavelet transform, thus providing a good signal processing tool for bearing defect diagnosis.

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