Магистерская диссертация тема работы Разработка цифрового двойника технологического процесса с использованием производственных данных

Продолжение приложения Б

(справочное)

Листинг кода для подготовки данных

Drive1_SD_CU.Speed = cleanedData2

% Fill outliers

[cleanedData,outlierIndices2] = filloutliers(Drive1_SD_CU.Current,'linear',...

'movmedian',days(1.5),'ThresholdFactor',1,'SamplePoints',Drive1_SD_CU.Time);

• 
  Display results clf
  

plot(Drive1_SD_CU.Time,Drive1_SD_CU.Current,'Color',[109 185 226]/255,...

'DisplayName','Input data') hold on

plot(Drive1_SD_CU.Time,cleanedData,'Color',[0 114 189]/255,'LineWidth',1.5,...

'DisplayName','Cleaned data')

• 
  Plot outliers
  

plot(Drive1_SD_CU.Time(outlierIndices2),Drive1_SD_CU.Current(outlierIndices2),'x',. 'Color',[64 64 64]/255,'DisplayName','Outliers')

title(['Number of outliers: ' num2str(nnz(outlierIndices2))])

hold off

legend

clear outlierIndices2

Drive1_SD_CU.Current = cleanedData

% Smooth input data

smoothedData = smoothdata(Drive1_SD_CU.Speed,'movmean','SmoothingFactor',0.25,...

'SamplePoints',Drive1_SD_CU.Time);

• 
  Display results clf
  

plot(Drive1_SD_CU.Time,Drive1_SD_CU.Speed,'Color',[109 185 226]/255,...

'DisplayName','Input data') hold on

plot(Drive1_SD_CU.Time,smoothedData,'Color',[0 114 189]/255,'LineWidth',1.5,...

'DisplayName','Smoothed data') hold off

legend

Drive1_SD_CU.Speed = smoothedData

• 
  Smooth input data
  

smoothedData = smoothdata(Drive1_SD_CU.Current,'movmean','SmoothingFactor',0.25,...

'SamplePoints',Drive1_SD_CU.Time);

• 
  Display results clf
  

plot(Drive1_SD_CU.Time,Drive1_SD_CU.Current,'Color',[109 185 226]/255,...

'DisplayName','Input data') hold on

plot(Drive1_SD_CU.Time,smoothedData,'Color',[0 114 189]/255,'LineWidth',1.5,...

'DisplayName','Smoothed data') hold off

legend

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