生產(chǎn)過(guò)程的動(dòng)態(tài)監(jiān)測(cè)灰色預(yù)測(cè)模型的一種應(yīng)用畢業(yè)論文外文翻譯

1、中文 中文3300字出處： 出處：The International Journal of Advanced Manufacturing Technology, 2006, 27(5-6): 543-546Sequential monitoring of manufacturing processes:an application of grey forecasting modelsLi-Lin Ku · Tung-Chen

2、HuangAbstractThis study used statistical control charts as an efficient tool for improving and monitoring the quality of manufacturing processes. Under the normality assumption, when a process variable is within control

3、limits, the process is treated as being in-control. Sometimes, the process acts as an in-control process for short periods; however, once the data show that the production process is out-of-control, a lot of defective pr

4、oducts will have already been produced, especially when the process exhibits an apparent normal trend behavior or if the change is only slight. In this paper, we explore the application of grey forecasting models for pre

5、dicting and monitoring production processes. The performance of control charts based on grey predictors for detecting process changes is investigated. The average run length(ARL) is used to measure the effectiveness when

6、 a mean shift exists. When a mean shift occurs, the grey predictors are found to be superior to the sample mean, especially if the number of subgroups used to compute the grey predictors is small. The grey predictor is a

7、lso found to be very sensitive to the number of subgroups.Keywords Average run length · Control chart · Control limit · Grey predictor1 IntroductionStatistical control charts have long been used as an eff

8、icient tool for improving and monitoring the quality of manufacturing processes. Traditional statistical process control (SPC) methods assume that the process variable is distributed normally, and that the observed data

9、are independent. Under the normality assumption, when the process variable is within the control limits, the process is treated as being in-control; otherwise, the process assumes that some changes have occurred, i.e., t

10、he process may be out-of-control.is called a white system when its information is totally clear. When a system’s information is totally unknown, it is called a black system. If a system’s information is partially known,

11、then it is called a grey system.In manufacturing processes, the operational conditions, facility reliability and employee behaviors are all factors that are impossible to be totally known or be fully under control. In or

12、der to control the system behavior, a grey model is used to construct an ordinary differential equation, and then the differential equation of the grey model is solved. By using scarce past data, the grey model can accur

13、ately predict the output. After the output is predicted, it can be checked if the process is under control or not.In this paper, we monitor and predict the process output by means of the GM(1,1) model [7]. Sequential mon

14、itoring is a procedure in which a new output point is chosen (usually it is a sample mean) and the cumulative results of the grey forecasts are analyzed before proceeding to the next new point. The procedure can be separ

15、ated into five steps:Step 1: Collect original data and build a data sequence. The observed original data are defined as , where is th sample mean. The raw sequence of samples is ) 0 ( i x i i kdefined as(1) ? ? ) 0 (

16、) 0 ( 3) 0 ( 2) 0 ( 1) 0 ( , , , k x x x x x ? ?Step 2: Transform the original data sequence into a new sequence. A new sequence is generated by the accumulated generating operation(AGO), where ) 1 ( x(2) ? ? ) 1 ( ) 1 (

17、 3) 1 ( 2) 1 ( 1) 1 ( , , , k x x x x x ? ?The is derived as follows: ) 1 ( i x(3) . , , 2 , 1 ,1) 0 ( ) 1 ( ??? ?inn i k i x x ?Step 3: Build a first-order differential equation of the GM(1,1) model. By transforming the