This template demonstrate how to build and deploy predictive maintenance models to predict asset failures.
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. As part of the Azure Machine Learning offering, Microsoft provides a template that helps data scientists easily build and deploy a predictive maintenance solution. **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.** The template includes a collection of pre-configured machine learning modules, as well as custom R scripts in the *Execute R Script* module, to enable an end-to-end solution from data processing to deploying of the machine learning model. 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 uses the example of simulated aircraft engine run-to-failure events to demonstrate the predictive maintenance modeling process. The implicit assumption of modeling data as done below is that the asset of interest has a progressing degradation pattern, which is reflected in the asset's sensor measurements. By examining the asset's sensor values over time, the machine learning algorithm can learn the relationship between the sensor values and changes in sensor values to the historical failures in order to predict failures in the future. We suggest examining the data format and going through all three steps of the template before replacing the data with your own. The template is divided into 3 separate steps with 7 experiments in total, where the first step has 1 experiment, and the other two steps each contains 3 experiments each addressing one of the modeling solutions. ![work flow](https://az712634.vo.msecnd.net/samplesimg/v1/T4/workflow.png)