Ayman PredictiveMaintenance

Simple and Generic training model and experiment for Predictive Maintenance (Preventative Maintenance). The training model is supervised model as features are engineered to drive the learning using a generic Stress Factor number (0:100) that represents the operational driver for failure or healthy state. A Planned maintenance state is denoted with **StressFactor**=0. Predictions can be obtained for a State attribute created in the model using R Module as a result of the relationship between StressFactor+StatusType+Cause field values and a Positive **DownTime** Value The Model uses Multiclass Logistic Regression algorithm to find out three probable value predictions for the State Where -1=Planned maintenance, 1=Healthy State and -2=Failure State prediction **Sample Data Format**:- AssetName,StressFactor,StatusType,Cause,Year,Month,DayOfYear,Hour,DownTime Cisco Platinum AC Power Supply,0,88,0,2011,1,1,10,381