scholarly journals Dynamic Prediction for Accuracy Maintaining Reliability of Superprecision Rolling Bearing in Service

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Liang Ye ◽  
Xintao Xia ◽  
Zhen Chang
Keyword(s):  
Time Series ◽  
Bootstrap Method ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Grey System Theory ◽  
Dynamic Monitoring ◽  
Grey System ◽  
Dynamic Prediction ◽  
The Future

A dynamic prediction method for accuracy maintaining reliability (AMR) of superprecision rolling bearings (SPRBs) in service is proposed by effectively fusing chaos theory and grey system theory and applying stochastic processes. In this paper, the time series of a vibration signal is used to characterize the state information for SPRB, and four runtime data points can be predicted in the future, which depends on four chaotic forecasting models to preprocess the time series. Using the grey bootstrap method and sampling from the four runtime data, a large amount of generated data (GD) are gained to analyze the changes in information on bearing service accuracy. Then, using a predefined accuracy threshold to match the Poisson count for the GD, the estimated value of variation intensity is obtained. Subsequently, with the help of the Poisson process, the dynamic evolution process is forecast in real time for AMR of the SPRB for each step in the future. Finally, according to a novel concept for maintaining relative reliability in an SPRB, the failure degree of a bearing maintaining an optimum accuracy status (BMOAS) is effectively described. Experimental investigation shows that multiple chaotic forecasting methods are accurate and feasible with all relative errors below 15%; the reliability of each step in the future can truly be described, and the prediction results for AMR over the same subseries show good consistency; dynamic monitoring of the health status of SPRB can be realized by the degree to which a BMOAS fails.

AGO-Based Time Series Prediction Method Using LS-SVR

2012 ◽  
Vol 220-223 ◽  
pp. 2133-2137
Author(s):  
Yang Ming Guo ◽  
Xiao Lei Li ◽  
Jie Zhong Ma
Keyword(s):  
Complex System ◽  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Health Condition ◽  
Grey System Theory ◽  
Good Prediction ◽  
Grey System ◽  
Complex Dynamic

Fault or health condition prediction of complex system equipments has attracted more and more attention in recent years. Complex system equipments often show complex dynamic behavior and uncertainty, it is difficult to establish precise physical model. Therefore, the time series of complex equipments are often used to implement the prediction in practice. In this paper, in order to improve the prediction accuracy, based on grey system theory, accumulated generating operation (AGO) with raw time series is made to improve the data quality and regularity, and then inverse accumulated generating operation (IAGO) is performed to get the prediction results with the sequence, which is computed by LS-SVR. The results indicate preliminarily that the proposed method is an effective prediction method for its good prediction precision.

scholarly journals Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Guo Yangming ◽  
Zhang Lu ◽  
Cai Xiaobin ◽  
Ran Congbao ◽  
Zhai Zhengjun ◽  
...  
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Computing Time ◽  
Grey System Theory ◽  
Good Prediction ◽  
Grey System ◽  
Online Prediction ◽  
Prediction Approach

Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS-SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.

Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

2022 ◽  
Vol 64 (1) ◽  
pp. 38-44
Author(s):  
Maosheng Gao ◽  
Zhiwu Shang ◽  
Wanxiang Li ◽  
Shiqi Qian ◽  
Yan Yu
Keyword(s):  
Real Time ◽  
Time Domain ◽  
Dynamic Time Warping ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Time Warping ◽  
Trend Prediction ◽  
Residual Signal ◽  
Dynamic Time

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

KPCA Denoising and its Application in Machinery Fault Diagnosis

2011 ◽  
Vol 103 ◽  
pp. 274-278 ◽  
Author(s):  
Ling Li Jiang ◽  
Zong Qun Deng ◽  
Si Wen Tang
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Kernel Principal Component Analysis ◽  
Reconstruction Method ◽  
Denoising Method ◽  
One Dimensional ◽  
Multidimensional Time Series ◽  
Machinery Fault Diagnosis

This paper proposes a kernel principal component analysis (KPCA)-based denoising method for removing the noise from vibration signal. Firstly, one-dimensional time series is expanded to multidimensional time series by the phase space reconstruction method. Then, KPCA is performed on the multidimensional time series. The first kernel principal component is the denoised signal. A rolling bearing denoising example verify the effectiveness of the proposed method

scholarly journals Evaluation of Dynamic Uncertainty of Rolling Bearing Vibration Performance

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Liang Ye ◽  
Xintao Xia ◽  
Zhen Chang
Keyword(s):  
Time Series ◽  
Chaos Theory ◽  
Maximum Entropy Method ◽  
Bootstrap Method ◽  
Rolling Bearing ◽  
Entropy Method ◽  
Performance Time ◽  
Dynamic Uncertainty ◽  
Bearing Vibration ◽  
Vibration Performance

The variation trend, failure trajectory, probability distribution, and other information vary with time and working conditions for rolling bearing vibration performance, which makes the evaluation and prediction of the evolution process difficult for the performance reliability. In view of this, the chaos theory, grey bootstrap method, and maximum entropy method were effectively fused to propose a mathematical model for the dynamic uncertainty evaluation of rolling bearing vibration performance. After reconstructing the phase space of the vibration performance time series, four local prediction methods were applied to predict the vibration values of bearings to verify the effectiveness and validity of chaos theory. The estimated true value and estimated interval were calculated using the grey bootstrap method (GBM) and maximum entropy method. Finally, the validity of the proposed model was verified by comparing the probability that the original data fall into the estimated interval with the given confidence level. The experimental results show that the proposed method can effectively predict the variation trend and failure trajectory of the vibration performance time series so as to realize the dynamic monitoring of the evolution process for rolling bearing vibration performance online.

Application of Grey System Theory in the Dynamic Monitoring of Bridge

2014 ◽  
Vol 501-504 ◽  
pp. 829-833
Author(s):  
Cheng Xin Yu ◽  
Zheng Wen Yu ◽  
Yong Qian Zhao ◽  
Jia Dong Zhang
Keyword(s):  
Dynamic Deformation ◽  
Active Role ◽  
Theory Model ◽  
Grey System Theory ◽  
Dynamic Monitoring ◽  
Grey Theory ◽  
Observation Error ◽  
Bridge Structure ◽  
Grey System ◽  
Safety And Reliability

It has great significance to ensure the safety of bridge structure by using information technology to monitor the bridge dynamic deformation and find out problems in time, besides, it is more effective to make analysis of the bridge by using grey theory model. The combination of the two methods could avoid the observation error, and play an active role in improving the safety and reliability of bridge.

Grey system theory-based models in time series prediction

2010 ◽  
Vol 37 (2) ◽  
pp. 1784-1789 ◽  
Author(s):  
Erdal Kayacan ◽  
Baris Ulutas ◽  
Okyay Kaynak
Keyword(s):  
System Theory ◽  
Time Series ◽  
Time Series Prediction ◽  
Grey System Theory ◽  
Grey System

Grey system based novel forecasting and portfolio mechanism on CSE

2016 ◽  
Vol 6 (2) ◽  
pp. 126-142 ◽  
Author(s):  
R.M. Kapila Tharanga Rathnayaka ◽  
D.M.K.N Seneviratna ◽  
Wei Jianguo
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Model Comparison ◽  
Model Building ◽  
Price Indices ◽  
Grey System ◽  
Content Type ◽  
Ex Post ◽  
Comparison Results ◽  
The Future

Purpose – Because of the high volatility with unstable data patterns in the real world, the ability of forecasting price indices is notoriously embarrassing and represents a major challenge with traditional time series mechanisms; especially, most of the traditional approaches are weak to forecast future predictions in the high volatile and unbalanced frameworks under the global and local financial depressions. The purpose of this paper is to propose a new statistical approach for portfolio selection and stock market forecasting to assist investors as well as stock brokers to predict the future behaviors. Design/methodology/approach – This study mainly takes an attempt to understand the trends, behavioral patterns and predict the future estimations under the new proposed frame for the Colombo Stock Exchange (CSE), Sri Lanka. The methodology of this study is carried out under the two main phases. In the first phase, constructed a new portfolio mechanism based on k-means clustering. In the second stage, proposed a nonlinear forecasting methodology based on grey mechanism for forecasting stock market indices under the high-volatile fluctuations. The autoregressive integrated moving average (ARIMA) predictions are used as comparison mode. Findings – Initially, the k-mean clustering was applied to pick out the profitable sectors running under the CSE and results indicated that BFI is more significant than other 20 sectors. Second, the MAE, MAPE and MAD model comparison results clearly suggested that, the newly proposed nonlinear grey Bernoulli model (NGBM) is more appropriate than traditional ARIMA methods to forecast stock price indices under the non-stationary market conditions. Practical implications – Because of the flexible nonlinear modeling capability, proposed novel concepts are more suitable for applying in various areas in the field of financial, economic, military, geological and agricultural systems for pattern recognition, classification, time series forecasting, etc. Originality/value – For the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies. However, the NGBM is better both in model building and ex post testing stagers under the s-distributed data patterns with limited data forecastings.

Research on Reliability of Equipments Based on Interval Prediction Method

2013 ◽  
Vol 330 ◽  
pp. 279-281
Author(s):  
Jian Fang Liu ◽  
Jian Hua Yang
Keyword(s):  
System Theory ◽  
Prediction Method ◽  
Grey System Theory ◽  
Reliability Prediction ◽  
Grey System ◽  
Design Decisions ◽  
Remarkable Effect ◽  
Interval Prediction ◽  
Design Reliability ◽  
Method Of Design

While reliability is an important aspect of equipment quality, it has a remarkable effect on the usability of equipments. Reliability can reflect equipments' ability of keeping performance. As a method of design, reliability prediction is the fundamental of design decisions. In this paper, the grey system theory is used to deal with the data of reliability, and the method of interval prediction to predicate the reliability of equipments and analyze how to improve its reliability.

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