A new Cr25Ni35Nb alloy critical failure time prediction method based on coercive force magnetic signature

2021 ◽  
pp. 168809
Author(s):  
Qi Wang ◽  
Guangpei Cong ◽  
Yunrong Lyu ◽  
Wei Yu
Keyword(s):  
Coercive Force ◽  
Failure Time ◽  
Prediction Method ◽  
Time Prediction ◽  
Magnetic Signature

scholarly journals Failure Prediction of Open-pit Mine Landslide Containing Complex Geological Structure using Inverse Velocity Method: A Case Study in West Open-Pit mine, Pingzhuang, China

2021 ◽  
Author(s):  
Han Du ◽  
Danqing Song
Keyword(s):  
Failure Time ◽  
Prediction Method ◽  
Geological Structure ◽  
Open Pit Mine ◽  
Open Pit ◽  
Landslide Prediction ◽  
Smoothing Filter ◽  
Time Prediction ◽  
Inverse Velocity Method ◽  
Inverse Velocity

Abstract In the field of open-pit geological risk management, landslide failure time prediction is one of the important topics. Based on the analysis of displacement monitoring data, the inverse velocity method (IVM) has become an effective method to solve this issue. In order to improve the reliability of landslide prediction, four filters were used to test the velocity time series, and the effect of landslide failure time prediction was compared and analyzed. The IVM is used to predict the failure time of open-pit coal mine landslide. The results show that the sliding process of landslide can be divided into three stages based on the IVM: the initial attenuation stage (regressive stage), the second attenuation stage (progressive stage), the linear reduction stage (autoregressive stage). The accuracy of the IVM is closely related to the measured noise of the monitoring equipment and the natural noise of the environment, which will affect the identification of different deformation stages. Compared with the raw data and the exponential smoothing filter (ESF) models, the fitting effect of short-term smoothing filter (SSF) and long-term smoothing filter (LSF) in the linear autoregressive stage is better. A slope displacement pixel difference method based on fitting accuracy and field monitoring signals is proposed to determine the point onset-of-acceleration (OOA) that is very important role for landslide prediction. A stratified prediction method combining SSF and LSF is proposed. The prediction method is divided into two levels, and the application of this method is given.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

Failure time prediction using adaptive logical analysis of survival curves and multiple machining signals

2018 ◽  
Vol 31 (2) ◽  
pp. 403-415 ◽  
Author(s):  
Ahmed Elsheikh ◽  
Soumaya Yacout ◽  
Mohamed-Salah Ouali ◽  
Yasser Shaban
Keyword(s):  
Failure Time ◽  
Logical Analysis ◽  
Survival Curves ◽  
Time Prediction

Failure Prediction Analysis on the 172 Basic Plane Based on Grey Theory

2014 ◽  
Vol 624 ◽  
pp. 366-370
Author(s):  
Li Qun Li ◽  
Hui Zhao
Keyword(s):  
Failure Time ◽  
Important Application ◽  
Grey Theory ◽  
Equipment Failure ◽  
Model Data ◽  
Safe Operation ◽  
Time Prediction ◽  
Basic Plane ◽  
Fault Information ◽  
Analysis Prediction

172 basic Cessna plane in the process of operation, the production of equipment failure is random, so the evaluation of equipment performance and to predict its failure time to improve the safe operation of the 172 basic plane has important application value. On the plane this complex system, the grey theory combined with 172 basic Cessna plane, the collection of 172 basic aircraft fault information centralized data processing, analysis, prediction model GM (1, 1), through the calculation of the GM model data, and the error precision fitting test, better realize the basic 172 aircraft equipment failure time prediction.

Real-Time Prediction Method for Performance Degradation Trend Based on Reliability Experimental Data

2013 ◽  
Vol 321-324 ◽  
pp. 757-761 ◽  
Author(s):  
Chen Liang Song ◽  
Zhen Liu ◽  
Bin Long ◽  
Cheng Lin Yang
Keyword(s):  
Experimental Data ◽  
Real Time ◽  
Field Data ◽  
Prediction Method ◽  
Field Method ◽  
Performance Degradation ◽  
New Method ◽  
Time Prediction ◽  
Prediction Result ◽  
The Relationship

According to the real-time prediction for performance degradation trend, the commonly used method is just based on field data. But this methods prediction result will not be so much ideal when the fitting of degradation trend of field data is not good. To solve the problem, the paper introduces a new method which is not only based on field method but also based on reliability experimental data coming from the history experiment. We use the relationship between the field data and reliability experimental data to get the result of the two kinds of data respectively and then get the weights according to the two prediction results. Finally, the final real-time prediction result for performance degradation tendency can obtain by allocating the weights to the two prediction results.

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