Fault Diagnosis of Motor Rotor Based on Fuzzy C-Means Clustering Analysis

2013 ◽  
Vol 273 ◽  
pp. 409-413 ◽  
Author(s):  
Yu Xiang Cao ◽  
Xue Jun Li ◽  
Ling Li Jiang
Keyword(s):  
Time Domain ◽  
Clustering Analysis ◽  
Statistical Features ◽  
Fuzzy C Means ◽  
Time Domain Signal ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Fault Diagnostic ◽  
Characteristic Features ◽  
The Time Domain

For the fuzziness of the fault symptoms in motor rotor, this paper proposes a fault diagnostic method which based on the time-domain statistical features and the fuzzy c-means clustering analysis (FCM). This method is to extract the characteristic features of time-domain signal via time-domain statistics and to import the extracted characteristic vector to classifier. And then the fuzzy c-means realizes the classification by confirming the distance among samples, which is based on the degree of membership between the sample and the clustering center. The fault diagnostic cases of motor rotor show that the method which bases on the time-domain statistical features-FCM can detect the rotor fault effectively and distinguish the different types of fault correctly. Therefore, it can be used as an important means of rotor fault identification.

scholarly journals Is the time domain signal-averaged electrocardiogram helpful in patients with ventricular tachycardia without apparent structural heart disease?

1995 ◽  
Vol 18 (10) ◽  
pp. 568-572 ◽  
Author(s):  
Yelena S. K. Orlov ◽  
Michael A. Brodsky ◽  
Michael V. Orlov ◽  
Byron J. Allen ◽  
Rex J. Winters
Keyword(s):  
Heart Disease ◽  
Ventricular Tachycardia ◽  
Time Domain ◽  
Structural Heart Disease ◽  
Time Domain Signal ◽  
The Time Domain

scholarly journals Ultrawideband Impulse Radar Through-the-Wall Imaging with Compressive Sensing

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Wenji Zhang ◽  
Moeness G. Amin ◽  
Fauzia Ahmad ◽  
Ahmad Hoorfar ◽  
Graeme E. Smith
Keyword(s):  
Compressive Sensing ◽  
Time Domain ◽  
Measurement Data ◽  
Acquisition Time ◽  
Time Domain Signal ◽  
Spatially Extended ◽  
Data Volume ◽  
Order Of Magnitude ◽  
New Perspective ◽  
The Time Domain

Compressive Sensing (CS) provides a new perspective for addressing radar applications requiring large amount of measurements and long data acquisition time; both issues are inherent in through-the-wall radar imaging (TWRI). Most CS techniques applied to TWRI consider stepped-frequency radar platforms. In this paper, the impulse radar two-dimensional (2D) TWRI problem is cast within the framework of CS and solved by the sparse constraint optimization performed on time-domain samples. Instead of the direct sampling of the time domain signal at the Nyquist rate, the Random Modulation Preintegration architecture is employed for the CS projection measurement, which significantly reduces the amount of measurement data for TWRI. Numerical results for point-like and spatially extended targets show that high-quality reliable TWRI based on the CS imaging approach can be achieved with a number of data points with an order of magnitude less than that required by conventional beamforming using the entire data volume.

scholarly journals Investigation of Light Parameters on Image Quality and Optical Coherence Tomography

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Boka Fikadu ◽  
Bulcha Bekele ◽  
Leta Tesfaye Jule ◽  
Anatol Degefa ◽  
N. Nagaprasad ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Image Quality ◽  
Time Domain ◽  
Glass Sample ◽  
Michelson Interferometer ◽  
Signal Strength ◽  
Optical Coherence ◽  
Time Domain Signal ◽  
Significant Difference ◽  
The Time Domain

In this work, image quality and optical coherence tomography were studied. The results of the study show that there is a very significant difference between ultrasound and optical coherence tomography to produce an image with a different wave. To understand this, we studied the basic principle of optical coherence tomography in the Michelson interferometer using monochromatic and broadband sources. Time-domain and spectral-domain measurements, which exist at the detector level, are briefly described using a glass sample. The time-domain signal strength of the Michelson interferometer using a broadband source is a Gaussian envelope.

Rolling Bearing Fault Diagnosis Research

2012 ◽  
Vol 155-156 ◽  
pp. 87-91
Author(s):  
Zhong Hu Yuan ◽  
Yang Su ◽  
Xiao Xuan Qi
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Recognition Rate ◽  
Principal Component ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Time Domain Signal ◽  
Feature Information ◽  
The Time Domain

According to the characteristics of the rolling bearing fault, we make the research on fault diagnosis. Time domain signal can not perform the fault feature information well. The power spectrum changes the time domain signals into the frequency signals. It sets up the new data model. It uses the principal component analysis on fault diagnosis. It uses T square statistics and Q statistics methods to make fault diagnosis. Simulation experiment results demonstrate that this method provides a high recognition rate.

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