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In the case of Digital Signal Distribution (DSD), machine learning algorithms have contributed to elaborate better ways to enable failure prediction. In this work a nested model for predicting failures in the components involved in DSD failure is presented. The failure can be caused by multiple and different components and also due to correlations between them. We propose a clustering model to isolate component behavior, and subsequently apply predictive models to each cluster. With principal components analysis and cluster analysis we have been able to identify group of failures' causes in this way failures can be segmented and treated properly. We found seven significant features for classification to determine which part is failing. The clustering process generated two groups that allow us to predict if a general failure is going to occur, and the classification process permits us to forecast which component is probably going to present a failure.

Methodology for the Model for Failure Prediction in a Digital Signal Distribution