What is principal component analysis in SAP IOT-PDM - Predictive Maintenance?

SAP Term: principal component analysis

• Component: IOT-PDM

• Component Name: Predictive Maintenance

• Description: A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components or sometimes, principal modes of variation. The number of principal components is less than or equal to the smaller of the number of original variables or the number of observations. This transformation is defined in such a way that the first principal component has the largest possible variance that is, accounts for as much of the variability in the data as possible, and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.

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• Key Concepts: 
  Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of large datasets. It is used to identify patterns in data and to reduce the complexity of the data by extracting the most important features. PCA is used in SAP's IOT-PDM Predictive Maintenance solution to identify patterns in sensor data and to reduce the complexity of the data. 
  
  How to use it: 
  PCA can be used to identify patterns in sensor data and to reduce the complexity of the data. It can be used to identify correlations between different variables, which can then be used to make predictions about future events. PCA can also be used to reduce the number of variables in a dataset, making it easier to analyze and interpret. 
  
  Tips & Tricks: 
  When using PCA, it is important to ensure that all variables are properly scaled before analysis. This will ensure that all variables are treated equally and that the results are more accurate. Additionally, it is important to consider the number of components that should be extracted from the dataset. Too few components may not capture all of the important features, while too many components may lead to overfitting. 
  
  Related Information: 
  PCA is closely related to other techniques such as factor analysis and cluster analysis. These techniques can also be used to identify patterns in data and reduce complexity. Additionally, PCA can be combined with other machine learning algorithms such as neural networks or support vector machines for more accurate predictions.