Predictive Maintenance Template

Yan Zhang • April 28, 2015

Decision Forest Regression , Two-Class Decision Forest , Boosted Decision Tree Regression , Poisson Regression , Neural Network Regression , Two-Class Logistic Regression , Two-Class Boosted Decision Tree , Two-Class Neural Network , Multiclass Logistic Regression , Multiclass Neural Network , Ordinal Regression

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Predictive maintenance encompasses a variety of topics, including but not limited to: failure prediction, failure diagnosis (root cause analysis), failure detection, failure type classification, and recommendation of mitigation or maintenance actions after failure. This predictive maintenance template focuses on the techniques used to predict when an in-service machine will fail, so that maintenance can be planned in advance. In particular, this template illustrates the process of predicting future failure events in the scenario of aircraft engine failures [1]. Three modeling solutions are provided in this template to accomplish the following tasks. -- Regression: Predict the Remaining Useful Life (RUL), or Time to Failure (TTF). -- Binary classification: Predict if an asset will fail within certain time frame (e.g. days). -- Multi-class classification: Predict if an asset will fail in different time windows: E.g., fails in window [1, w0] days; fails in the window [w0+1,w1] days; not fail within w1 days The time units mentioned above can be replaced by working hours, cycles, mileage, transactions, etc. based on the actual scenario. This template provides a complete view of the process of data input, data enhancement (data labeling, feature engineering, feature selection, etc.), model construction and evaluation. The individual steps of this template, together with the web service deployment procedure can be found in following step by step experiments in Gallery: Predictive Maintenance: Step 1 of 3, data preparation and feature engineering: http://go.microsoft.com/fwlink/?LinkID=534243 Predictive Maintenance: Step 2A of 3, train and evaluate regression models: http://go.microsoft.com/fwlink/?LinkID=534245 Predictive Maintenance: Step 2B of 3, train and evaluate binary classification models: http://go.microsoft.com/fwlink/?LinkID=534247 Predictive Maintenance: Step 2C of 3, train and evaluation multi-class classification models: http://go.microsoft.com/fwlink/?LinkID=534249 Predictive Maintenance: Step 3A of 3, deploy web service with a regression model: http://go.microsoft.com/fwlink/?LinkID=534251 Predictive Maintenance: Step 3B of 3, deploy web service with a binary classification model: http://go.microsoft.com/fwlink/?LinkID=534253 Predictive Maintenance: Step 3C of 3, deploy web service with a multi-class classification model: http://go.microsoft.com/fwlink/?LinkID=534255 [1] A. Saxena and K. Goebel (2008). "Turbofan Engine Degradation Simulation Data Set", NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/), NASA Ames Research Center, Moffett Field, CA Created by a Microsoft Employee