Research on equipment failure rate forecasting based on gray-linear regression combined model

2016 ◽  
Author(s):  
Kunpeng Bi ◽  
Guohui Yan ◽  
Hongyuan Zhang ◽  
Na Tang
Keyword(s):  
Linear Regression ◽  
Failure Rate ◽  
Equipment Failure ◽  
Combined Model

scholarly journals Method for Predicting Failure Rate of Airborne Equipment Based on Optimal Combination Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
WenQiang Li ◽  
Ning Hou ◽  
XiangKun Sun
Keyword(s):  
Failure Rate ◽  
Health Management ◽  
Multiple Linear Regression Model ◽  
Optimal Combination ◽  
Support Vector ◽  
Equipment Failure ◽  
Combined Model ◽  
Machine Model ◽  
Partial Least Squares Model ◽  
Comparison Functions

Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate.

scholarly journals MODELLING OF EQUIPMENT FAILURE RATE ACCOUNTING FOR THE UNCERTAINTY

2015 ◽  
Vol 8 (3) ◽  
pp. 1484-1504
Author(s):  
H.X. Tian ◽  
W.F Wu ◽  
P. Wang ◽  
H.Z. Li
Keyword(s):  
Failure Rate ◽  
Equipment Failure

scholarly journals MARSplines method as a tool for failure frequency modelling

2018 ◽  
Vol 44 ◽  
pp. 00086
Author(s):  
Małgorzata Kutyłowska
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Failure Rate ◽  
Basis Function ◽  
Adaptive Algorithm ◽  
Point Of View ◽  
Water Utility ◽  
Failure Frequency ◽  
Rate Prediction ◽  
Operational Data

The paper presents the results of failure rate prediction using adaptive algorithm MARSplines. This method could be defined as segmental and multiple linear regression. The range of segments defines the range of applicability of that methodology. On the basis of operational data received from Water Utility two separate models were created for distribution pipes and house connections. The calculations were carried out in the programme Statistica 13.1. Maximal number of basis function was equalled to 30; so-called pruning was used. Interaction level equalled to 1, the penalty for adding basis function amounted to 2, and the threshold – 0.0005. GCV error equalled to 0.0018 and 0.0253 as well as 0.0738 and 0.1058 for distribution pipes and house connections in learning and prognosis process, respectively. The prediction results in validation step were not satisfactory in relation to distribution pipes, because constant value of failure rate was observed. Concerning house connections, the forecasting was slightly better, but still the overestimation seems to be unacceptable from engineering point of view.

Application of combined model with DGM(1,1) and linear regression in grain yield prediction

2018 ◽  
Vol 8 (1) ◽  
pp. 25-34 ◽  
Author(s):  
Bingjun Li ◽  
Weiming Yang ◽  
Xiaolu Li
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Grain Yield ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Entropy Weight ◽  
Combined Model ◽  
Content Type ◽  
Combination Model

Purpose The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations. Design/methodology/approach Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values. Findings The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction. Practical implications The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed. Originality/value This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

The Settlement Prediction of Pile Foundation Based on Grey Linear Regression Model

2012 ◽  
Vol 204-208 ◽  
pp. 320-325
Author(s):  
Jia Kun Liu ◽  
Jian Ping Wang ◽  
Min Zhu ◽  
Xiao Jie Hou
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Pile Foundation ◽  
Load Test ◽  
Static Load Test ◽  
Combined Model ◽  
Actual Meaning ◽  
Relationship Of ◽  
The Relationship

Grey linear regression model is a covert grey combined model that is built based on GM(1,1) model and linear regression model. It improves undervaluation of linear regression model which can not in press the exponential growth and come to deficiency of grey GM (1, 1) model which has not linear factor. This paper briefly introduces the establishment and precision examination method of the grey linearity regression model and establishes the grey linear regression model to predict the relationship of load and settlement. Based on the data of static load test, the load-settlement curve is simulated and analyzed. The result of study shows that Grey Linear regression Model can effectively predict the settlement of pile foundation, and be of the theoretical and actual meaning for further analyzing the bearing capability of pile foundation.

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