scholarly journals A Character of Rotating Machinery Defined Based on KICA

2011 ◽  
Vol 2-3 ◽  
pp. 761-764
Author(s):  
Ling Li Jiang ◽  
Zong Qun Deng ◽  
Si Wen Tang
Keyword(s):  
Correlation Coefficient ◽  
Failure Modes ◽  
Rolling Bearing ◽  
Coefficient Matrix ◽  
Rotating Machinery ◽  
Good Effect ◽  
Gear Fault ◽  
Sensitive Character ◽  
Machinery Fault Diagnosis ◽  
Correlation Coefficient Matrix

This research aims at defining a character of rotating machinery——KIC, that the typical rolling bearing and gear failure modes can be effectively identified by using the character. Firstly, A correlation coefficient matrix is composed by the correlation coefficient between two-two inde-pendent component derived from kernel independent component (KICA). Then the KIC is defined by the correlation coefficient matrix. Experimental results show that the KIC has a good effect for identifying the bearing and gear fault modes, so it can be used as sensitive character for rotating machinery fault diagnosis.

Choose the Best Coach Based on the Theory of Grey System

2014 ◽  
Vol 998-999 ◽  
pp. 1757-1760 ◽  
Author(s):  
Jia Zhao
Keyword(s):  
Correlation Coefficient ◽  
Evaluation Model ◽  
Relative Size ◽  
Coefficient Matrix ◽  
Weight Matrix ◽  
Target Range ◽  
Grey System ◽  
Average Value ◽  
Dynamic Weight ◽  
Correlation Coefficient Matrix

In order to select “the best all time college coach” across both genders and all possible sports, we establish a multi-hierarchy evaluation model based on the theory of grey system. Without affecting the reliability of the results, we analyze the distribution of every index to determine the initial screening standard, narrow target range and simplify the problem. Then, we use the theory of grey system to evaluate coaches. We first normalize all indexes to make them comparable. Second, we determine a set of the best indexes as the reference vector and obtain the correlation coefficient matrix. On the issue of the weight of all indexes, we introduce the concept of dynamic weight matrix by considering the distribution and relative size of them. After that, we obtain the value of the final evaluation from the correlation coefficient matrix and dynamic weight matrix .As for the influence of time on the evaluation, we regroup the data of coaches according to the time section for five years, which results in some statistical variables, and then we draw the trend graph of them. To sum up, the number of excellent coaches of every sport is increasing over time, but the average value and standard deviation of winning percentage is decreasing. The model has the adaptability on different sports by comparison. And because the data of women coaches is insufficient, the influence of genders on the evaluation needs to be researched further.

Vibration Signal Analysis of Bearing Based on EMD and Resonance Demodulation

2014 ◽  
Vol 556-562 ◽  
pp. 1286-1289 ◽  
Author(s):  
Jie Shi ◽  
Xing Wu ◽  
Nan Pan ◽  
Sen Wang ◽  
Jun Zhou
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Experimental Station ◽  
Fault Type ◽  
Mode Function ◽  
Vibration Signal Analysis

In order to monitor the operation state and implement fault diagnosis of rolling bearing in rotating machinery, this paper presents a method of fault diagnosis of rolling bearing, which is based on EMD and resonance demodulation. Using EMD to decompose the signal, which comes from QPZZ-II experimental station, the components of intrinsic mode function (IMF) will be obtained. Then, calculating the correlation coefficient of each IMF component, the highest correlation coefficient of IMF component will be analyzed by resonance demodulation. Finally, the experimental results show that the method can accurately identify and diagnose the running state and bearing fault type.

scholarly journals Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peiming Shi ◽  
Cuijiao Su ◽  
Dongying Han
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Good Effect ◽  
Resonance System ◽  
Mode Decomposition ◽  
Eemd Method

An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper. Firstly, the stochastic resonance system is optimized by particle swarm optimization (PSO), and the best structure parameters are obtained. Then, the signal with noise is put into the stochastic resonance system and denoising and enhancing the signal. Secondly, the signal output from the stochastic resonance system is extracted by analytical mode decomposition (AMD) method. Finally, the signal is decomposed by ensemble empirical mode decomposition (EEMD) method. The simulation results show that the optimal stochastic resonance system can effectively improve the signal-to-noise ratio, and the number of effective components of EEMD decomposition is significantly reduced after using AMD, thus improving the decomposition results of EEMD and enhancing the amplitude of components frequency. Through the extraction of the rolling bearing fault signal feature proved that the method has a good effect.

Trend Extraction in Vibration Signal Based on EMD

2012 ◽  
Vol 459 ◽  
pp. 377-380
Author(s):  
Yu Hua Dong ◽  
Jun Xing Zhang
Keyword(s):  
Correlation Coefficient ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Coefficient Matrix ◽  
Trend Extraction ◽  
Mode Decomposition ◽  
Effective Combination ◽  
Correlation Coefficient Matrix

This paper proposed a de-trend method for vibration signal of telemetry based on the empirical mode decomposition (EMD) by correlation coefficient matrix. The signal is decomposed to a series of intrinsic mode component and the remainder item by EMD. It mainly distinguishes between the remainder item and the signal trend, according to the correlation coefficient matrix to determine whether some intrinsic mode component belongs to the trend item or not. The results show that signal trends can be extracted accurately through the effective combination EMD with correlation coefficient matrix and the proposed method has good applicability for different signals and different trends.

Correlation Coefficient Matrix Based PD Feature Dimensional Reduction

2014 ◽  
Vol 945-949 ◽  
pp. 2499-2504
Author(s):  
Yu Wang ◽  
Jin Sha Yuan ◽  
Hai Kun Shang ◽  
Song Jin
Keyword(s):  
Correlation Coefficient ◽  
Power Transformer ◽  
Partial Discharge ◽  
Back Propagation ◽  
Coefficient Matrix ◽  
Back Propagation Neural Network ◽  
Small Samples ◽  
Diagnostic Feature ◽  
Dimension Characteristic ◽  
Correlation Coefficient Matrix

The abstraction of diagnostic feature from field condition monitoring data is a significant research challenge. A new dimension reduction method based on correlation coefficient matrix is proposed aimed at the high-dimension characteristic parameters in the process of pattern recognition for partial discharge in power transformer. The CCM is constructed by parameters extracted from partial discharge signature in power transformer. The parameters that have similar classification characters are reduced directed by the correlation analysis result. The reduced PD features are inputted to the pattern classifiers of probabilistic neural networks (PNN). The results show that the parameter dimension is reduced and the classifier construction is simplified, and the recognition effect is better than that of the traditional back propagation neural network (BPNN) in the condition of small samples.

A New Method of Porous Space Reconstruction Using Multipoint Histogram Technology

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Na Zhang ◽  
Qian Sun ◽  
Mohamed Fadlelmula ◽  
Aziz Rahman ◽  
Yuhe Wang
Keyword(s):  
Correlation Coefficient ◽  
Pore Space ◽  
Coefficient Matrix ◽  
Training Image ◽  
Porous Space ◽  
Stable Theory ◽  
Pore Scale ◽  
New Construction ◽  
Point Correlation ◽  
Correlation Coefficient Matrix

Pore-scale modeling is becoming a hot topic in overall reservoir characterization process. It is an important approach for revealing the flow behaviors in porous media and exploring unknown flow patterns at pore scale. Over the past few decades, many reconstruction methods have been proposed, and among them the simulated annealing method (SAM) is extensively tested and easier to program. However, SAM is usually based on the two-point probability function or linear-path function, which fails to capture much more information on the multipoint connectivity of various shapes. For this reason, a new reconstruction method is proposed to reproduce the characteristics of a two-dimensional (2D) thin section based on the multipoint histogram. First, the two-point correlation coefficient matrix will be introduced to determine an optimal unit configuration of a multipoint histogram. Second, five different types of seven-point unit configurations will be used to test the unit configuration selection algorithm. Third, the multipoint histogram technology is used for generating the porous space reconstruction based on the prior unit configuration with a different calculation of the objective function. Finally, the spatial connectivity, patterns reproduction, the local percolation theory (LPT), and hydraulic connectivity are used to compare with those of the reference models. The results show that the multipoint histogram technology can produce better multipoint connectivity information than SAM. The reconstructed system matches the training image very well, which reveals that the reconstruction captures the geometry and topology information of the training image, for instance, the shape and distribution of pore space. The seven-point unit configuration is enough to get the spatial characters of the training image. The quality of pattern reproduction of the reconstruction is assessed by computing the multipoint histogram, and the similarity is around 97.3%. Based on the LPT analysis, the multipoint histogram can describe the anticipated patterns of geological heterogeneities and reproduce the connectivity of pore media with a high degree of accuracy. The two-point correlation coefficient matrix and a new construction theory are proposed. The new construction theory provides a stable theory and technology guidance for the study of pore space development and multiphase fluid flow rule in the digital rock.

scholarly journals Seasonal Distribution of Phytoplankton in Riwada Reservoir, Visakhapatnam, Andhra Pradesh, India

2013 ◽  
Vol 5 (3) ◽  
pp. 290-295 ◽  
Author(s):  
Jyothi KAPARAPU ◽  
Mohan Narasimha Rao GEDDADA
Keyword(s):  
Correlation Coefficient ◽  
Andhra Pradesh ◽  
Negative Relationship ◽  
Oxygen Demand ◽  
Diversity Indices ◽  
Coefficient Matrix ◽  
Total Hardness ◽  
Species Evenness ◽  
Total Phytoplankton ◽  
Correlation Coefficient Matrix

The present study deals with seasonal variations, correlation coefficient and biodiversity indices of phytoplankton during April 2011 to March 2012 in the Riwada reservoir, Visakhapatnam, Andhra Pradesh, India. Sampling was performed at five stations during pre-monsoon, monsoon and post monsoon. There were a total of 57 genera belonging to four major groups i.e., Chlorophyceae (27 genera), Bacillariophyceae (14 genera), Cyanophyceae (13 genera) and Euglenophyceae (three genera). Maximum and minimum total phytoplankton population and percentages were recorded at station three in pre monsoon and at station two during monsoon. The maximum and minimum species richness (Menhinick index R2) were found to be 1.29 at station one and 1.10 at station three respectively. Maximum and minimum species diversity (H1) were found at station four (3.98) and station two (3.71). Maximum species evenness was recorded at stations one, being four and five; minimum species evenness was recorded at station two. Correlation coefficient matrix indicated significant positive relationship with water temperature, pH, transparency, biological oxygen demand and chlorides, negative relationship with electric conductivity, total solids, total dissolved solids, total hardness, dissolved oxygen, nitrates, sulphates and phosphates of water. The diversity indices showed that the reservoir have a well balanced phytoplankton community.

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