scholarly journals Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of Machines

2008 ◽  
Vol 18 (4) ◽  
pp. 569-580 ◽  
Author(s):  
Czesław Cempel
Keyword(s):  
Condition Monitoring ◽  
Moving Average ◽  
Grey System Theory ◽  
Neural Nets ◽  
Grey System ◽  
Vibration Condition ◽  
Limit Value ◽  
Modes Of Failure ◽  
Grey Forecasting ◽  
Observation Matrix

Decomposition Of The Symptom Observation Matrix And Grey Forecasting In Vibration Condition Monitoring Of MachinesWith the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine conditions. On this basis, we can form a symptom observation matrix (SOM) intended for condition monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information about faults from the symptom observation matrix by means of singular value decomposition (SVD), in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM is applied with success. An attempt to assess the diagnostic contribution of a primary symptom is made, and also an approach to assess the symptom limit value and to connect the SVD methodology with neural nets is considered. Finally, a condition forecasting problem is discussed and an application of grey system theory (GST) to symptom prognosis is presented. These possibilities are illustrated by processing data taken directly from the machine vibration condition monitoring area.

Decomposition of Symptom Observation Matrix and its Optimization in Vibration Condition Monitoring of Machines

2007 ◽  
Vol 9 ◽  
pp. 51-60 ◽  
Author(s):  
Czesław Cempel
Keyword(s):  
Condition Monitoring ◽  
Moving Average ◽  
Neural Nets ◽  
Vibration Condition ◽  
Modes Of Failure ◽  
Observation Matrix ◽  
Value Decomposition ◽  
Almost All ◽  
Fault Information ◽  
Modern Metrology

With the modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of measuring quantities can be symptoms of machine condition. On this basis we can form the symptom observation matrix (SOM) intended for condition monitoring. On the other hand we know, that contemporary complex machines may have many modes of failure, so called faults. The paper presents a method for the extraction of fault information from the symptom observation matrix by means of singular value decomposition (SVD) in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM was applied with success. The attempt to assess the diagnostic contribution of primary symptom was undertaken, and also some approach to connect SVD methodology with neural nets is considered. These possibilities are illustrated in the paper by processing data taken directly from the vibration condition monitoring of the machine.

Study on the second grey relational grade and its properties

Kybernetes ◽  
2010 ◽  
Vol 39 (8) ◽  
pp. 1330-1335 ◽  
Author(s):  
Yan Ma
Keyword(s):  
System Theory ◽  
Design Methodology ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Grey System ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Practical Implications

PurposeThe purpose of this paper is to propose a second relational grade based on the general grey relational grade and analyze several of its properties.Design/methodology/approachGrey system theory. The paper proposes and studies second grey relational grade, establishes second grey relational formula, and studies several characteristics of second grey relational formula.FindingsProposing a second relational grade proved it could solve the problem of the parallelism partly and weaken relativity of space position.Research limitations/implicationsUntil now, the problem of the consistency could not be solved, nor could the problem of the effect which keeps the sequence the same.Practical implicationsThe precision of the grey forecasting model could be strengthened if used in the forecasting model.Originality/valueThe general relational grade only thinks over the relation between two sequences but does not involve the relation in one sequence. The second relational grade considers these two, so if the forecasting model is established with it, the model should be more exact.

Analysis and Models for the Friction Coefficient of a Sliding Bearing

2013 ◽  
Vol 395-396 ◽  
pp. 826-830
Author(s):  
Bao Ming Wang ◽  
Jin Xin Xu ◽  
Fei Zhu ◽  
Zai Xin Wu
Keyword(s):  
Friction Coefficient ◽  
Rotational Speed ◽  
Moving Average ◽  
Grey System Theory ◽  
Grey System ◽  
Sliding Bearings ◽  
Sliding Bearing ◽  
Non Linear ◽  
Grey Relational Grade ◽  
Grey Relational

In this paper, the analyzing and modeling of the friction coefficient in sliding bearing is reported. Based on the grey system theory, the effects of rotational speed and load on the friction coefficient of the sliding bearings are investigated. The grey relational grade is an important parameter to measure the effects of rotational speed and load on friction coefficient of the sliding bearings. The grey relational grade analysis shows that load has an even more significant effect upon the friction coefficient compared with rotational speed. On the basis of analyzing and processing the experimental data, a nonlinear model for friction coefficient of a sliding bearing have been set up by NARMAX Non-Linear Auto-Regressive Moving Average with Exogenous Input. It was found that the NARMAX Non-Linear model has good accuracy and is applicable for the calculation of friction coefficient in sliding bearing.

Coordination of Construction Project Cost and Schedule Based on Grey Forecasting Method

2013 ◽  
Vol 357-360 ◽  
pp. 2259-2266
Author(s):  
Chun Ling Sun ◽  
Hong Song ◽  
Rui Tian
Keyword(s):  
Control Method ◽  
Construction Project ◽  
Grey System Theory ◽  
Grey System ◽  
Schedule Control ◽  
Project Cost ◽  
Grey Forecasting ◽  
Planning Optimization ◽  
Construction Project Cost ◽  
The Cost

In the premise of quality establishment, cost control and schedule control are the major goals of the construction project management. This paper is based on grey system theory as the foundation, and uses the grey control method to realize the cost and schedule control and establishes the coordination of GM (1, 1) prediction model, which is the core of the system. The GM (1, 1) grey forecasting model and network planning optimization combination as the effective date rectification control put forward the future control direction. Finally, it uses the case to evaluate the feasibility and rationality of the project cost and schedule control scheme.

Durability Prediction of Asphalt Mixture under Freeze-Thaw Cycle Based on GM (1,N) Grey Model

2015 ◽  
Vol 744-746 ◽  
pp. 1244-1248 ◽  
Author(s):  
Rui Xiong ◽  
Lu Wang
Keyword(s):  
System Theory ◽  
Asphalt Mixture ◽  
Grey System Theory ◽  
Grey System ◽  
Freeze Thaw ◽  
Splitting Strength ◽  
Grey Forecasting ◽  
Thaw Cycle ◽  
Freeze Thaw Cycle ◽  
Durability Prediction

In order to predict the durability of asphalt mixture under freeze-thaw cycles, the porosity and splitting strength of asphalt mixture under freeze-thaw cycle are studied, and GM(1, N) model in grey system theory is established. The results show that there is a good correlation between the porosity and the splitting strength in the durability factors of asphalt mixture. The value of the porosity increases with the increasing of the freeze-thaw times, and the value of the splitting strength decreased with the increasing of the freeze-thaw times. The GM(1, N) grey forecasting model can predict the porosity and the splitting strength well, and the calculated results agree well with the experimental data. Therefore, it is feasible to introduce the grey system theory in the prediction study of the durability of asphalt mixture.

scholarly journals The Development of a Hybrid Wavelet-ARIMA-LSTM Model for Precipitation Amounts and Drought Analysis

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 74
Author(s):  
Xianghua Wu ◽  
Jieqin Zhou ◽  
Huaying Yu ◽  
Duanyang Liu ◽  
Kang Xie ◽  
...  
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Moving Average ◽  
Grey System Theory ◽  
Small Sample ◽  
Annual Precipitation ◽  
Drought Event ◽  
Monthly Precipitation ◽  
Grey Theory ◽  
Grey System

Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting.

Grey System Theory Modeling for Nonlinear, Dynamic Machine Tool Thermal Error

2009 ◽  
Vol 407-408 ◽  
pp. 112-116 ◽  
Author(s):  
Jia Yu Yan ◽  
Jian Guo Yang
Keyword(s):  
System Theory ◽  
Dynamic Models ◽  
Moving Average ◽  
Back Propagation ◽  
Model Performance ◽  
Thermal Error ◽  
Back Propagation Neural Network ◽  
Grey System Theory ◽  
Grey System ◽  
Elastic Process

To accommodate the nonlinear and dynamic nature of thermal elastic process, Grey System Theory (GST) is adopted. By using this theory, GM (2, 1) and GM (1, 4) models are constructed. Real cutting experiment on a turning machine is conducted to establish and validate the model performance in terms of generalization ability. The comparison indicates that GM (2, 1) and GM (1, 4) perform better than other static and dynamic models such as Back Propagation Neural Network (BP) and Auto-regression Moving Average (ARMA). In addition, each of the two proposed model has their own advantages and they can be applied in practice.

Research of Accidents due to Human Factor in Coal Mine Based on GM Model

2015 ◽  
Vol 1092-1093 ◽  
pp. 692-695 ◽  
Author(s):  
Shang Quan Ma ◽  
Jing Fu
Keyword(s):  
System Theory ◽  
Incomplete Information ◽  
Coal Mine ◽  
Human Factor ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Grey System ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Development Tendency

Grey forecast can master the developing law of system through dealing with incomplete information of system at present. On the basis of actual data of Feng Feng Coal Mine, the grey forecasting model for coal mine accidents due to human factor in Feng Feng by using the grey system theory in this paper, it is shown that models which are built have good precisions. Safety and production of Feng Feng Coal Mine are forecasted by using grey forecasting models which are built. The results show that the forecasting models will help coal mines to forecast accidents due to human factor next year and generally tally with development tendency of Feng Feng Coal Mine.

Sign in / Sign up

Export Citation Format

Share Document