Dissimilarity Measures for ICA-Based Source Number Estimation

2012 ◽  
Author(s):  
Wei Cheng ◽  
Seungchul Lee ◽  
Zhousuo Zhang ◽  
Zhengjia He
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
A Priori ◽  
Source Separation ◽  
Mechanical Vibration ◽  
Estimation Method ◽  
Dissimilarity Measures ◽  
Source Number Estimation ◽  
Machinery Condition Monitoring ◽  
Source Number

Most of blind source separation problems are carried out with a priori knowledge of the source numbers. However, for source separation-based machinery condition monitoring and fault diagnosis, it is a challenge work to determine the number of sources for a well source separation due to complex structures and nonlinear mixing mode. Therefore, source number estimation is a necessary and important procedure prior to source separation and further diagnosis work. In this paper, we focus on a novel source number estimation method based on independent component analysis (ICA) and clustering evaluation analysis, and investigate the performances of different dissimilarity measures of ICA-based source number estimations with typical mechanical vibration signals. Our work contributes to find an effective solution of source number estimation for source separation-based machinery condition monitoring and fault diagnosis.

scholarly journals Analysis of the feasibility of an array of MEMS microphones to machinery condition monitoring or fault diagnosis

2017 ◽  
Vol 141 (5) ◽  
pp. 3958-3958
Author(s):  
Lara del-Val ◽  
Alberto Izquierdo ◽  
Juan J. Villacorta ◽  
Luis Suarez ◽  
Marta Herráez
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Machinery Condition Monitoring ◽  
Mems Microphones

The Use of Blind-Source-Separation Algorithm for Mechanical Signal Separation and Machine Fault Diagnosis

2003 ◽  
Author(s):  
Peter W. Tse ◽  
Jinyu Zhang
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Condition Monitoring ◽  
Source Separation ◽  
Simulated Data ◽  
Vibration Signal ◽  
Advantages And Disadvantages ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Mechanical Components

Vibration based machine fault diagnosis is widely adopted in machine condition monitoring. Since a machine is usually composed of many mechanical components, during the machine running, each component will generate its vibration and transmit to other components thru the shaft or linkages. Hence, the vibration signal collected from a sensor is the aggregation of all generated vibrations. To enhance the accuracy in vibration based machine fault diagnosis, the vibration generated by each component must be isolated and identified. In this paper, the performance of blind-source-separation (BSS) in separating various mixed sources is discussed. The BSS based method of second order statistics (SOS) has been applied to separate the aggregated vibration signals generated from a number of mechanical components. To verify the effectiveness of the BSS based SOS, a number of experiments were conducted using both simulated data and vibration generated form the industrial machines. The results show that the BSS possesses the ability to separate both artificially and naturally mixed signals. Such ability is definitely welcome in the fields of condition monitoring and maintenance. Moreover, the paper also discusses the advantages and disadvantages of the algorithm in the applications of machine fault diagnosis and future improvements.

A New Method for Adaptive Blind Source Separation Based on the Estimated Number of Dynamic Fault Sources

2012 ◽  
Vol 233 ◽  
pp. 211-217 ◽  
Author(s):  
Xiao Yan Yang ◽  
Xiong Zhou ◽  
Yi Ke Tang
Keyword(s):  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Estimation Algorithm ◽  
Accurate Estimation ◽  
Separation Algorithm ◽  
Underdetermined Blind Source Separation ◽  
Source Signal ◽  
Fault Source ◽  
Source Number

In fault diagnosis of large rotating machinery, the number of fault sources may be subject to dynamic changes, which often lead to the failure in accurate estimation of the number of sources and the effective isolation of the fault source. This paper introduced the expansion of the fourth-order cumulant matrices in estimating the dynamic fault source number, plus the relationship between the source signal number and the number of sensors being utilized in the selection of the blind source separation algorithm to achieve adaptive blind source separation. Experiments showed that the source number estimation algorithm could be quite effective in estimating the dynamic number of fault sources, even in the underdetermined condition. This adaptive blind source separation algorithm could then effectively achieve fault diagnosis in respect to the positive-determined, overdetermined and underdetermined blind source separation.

Shaft Vibration Signal Denoising Based on Wavelet Analysis

2013 ◽  
Vol 300-301 ◽  
pp. 1110-1113
Author(s):  
Tie Qiang Sun ◽  
Rong Liu ◽  
Zhi Qi Qiu
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Wavelet Packet ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Axial Vibration ◽  
Denoising Method ◽  
Original Source ◽  
Separation Result

In actual , there exist inevitably a lot of interference from neighbor machine and noise from surrondings in mechanical vibration signal measured by sensor ,which is disadvantageous for condition monitoring and fault diagnosis. In order to eliminate the axial vibration signal in the noise, using Wavelet packet denoising method in this article, Emulating experiment s were carried out under the MATLAB software ,original signals adopted vibration impulsion signal produced by vice position of faulty bear. Separation result s confirm this method successfully ext ract original source ,efficiently removes noise.

A source number estimation method for single optical fiber sensor

2015 ◽  
Author(s):  
Junpeng Hu ◽  
Zhiping Huang ◽  
Shaojing Su ◽  
Yimeng Zhang ◽  
Chunwu Liu
Keyword(s):  
Optical Fiber ◽  
Optical Fiber Sensor ◽  
Estimation Method ◽  
Fiber Sensor ◽  
Source Number Estimation ◽  
Source Number

A New Vibration Source Number Estimation Method Based on FFT

2011 ◽  
Vol 55-57 ◽  
pp. 1609-1612
Author(s):  
Hong Xian Ye ◽  
Xiao Ping Hu ◽  
Xiao Feng Zhou
Keyword(s):  
Frequency Spectrum ◽  
Fourier Transformation ◽  
Estimation Method ◽  
Fast Fourier Transformation ◽  
Vibration Source ◽  
Vibration Signals ◽  
The Real ◽  
Source Number Estimation ◽  
Spectrum Characteristic ◽  
Source Number

A new vibration source number estimation method based on Fast Fourier Transformation (FFT) is proposed. The frequency spectrum characteristic of vibration signals on a machine was analyzed. The characters of the vibration sources were obtained by means of FFT, which was used to estimate the number of vibration sources. The estimations illustrate that the presented method can obtain the correct source number not only under the condition of less source number than that of measurements. The most important is that it can get the source number correctly on the case of more source number than measurements’. The method gives a new idea to estimate the real vibration sources number.

A new source number estimation method based on the beam eigenvalue

2007 ◽  
Vol 6 (1) ◽  
pp. 41-46 ◽  
Author(s):  
Lei Jiang ◽  
Ping Cai ◽  
Juan Yang ◽  
Yi-ling Wang ◽  
Dan Xu
Keyword(s):  
Estimation Method ◽  
Source Number Estimation ◽  
Source Number
Sign in / Sign up

Export Citation Format

Share Document