Cortana Analytics Solution Template Playbook for predictive maintenance Posted on : Nov 21 - 2015

Predictive maintenance is one of the most demanded applications of predictive analytics with unarguable benefits including tremendous amount of cost savings. This playbook aims at providing a reference for predictive maintenance solutions with the emphasis on major use cases. It is prepared to give the reader an understanding of the most common business scenarios of predictive maintenance, challenges of qualifying business problems for such solutions, data required to solve these business problems, predictive modeling techniques to build solutions using such data and best practices with sample solution architectures. It also describes the specifics of the predictive models developed such as feature engineering, model development and performance evaluation. In essence, this playbook brings together the business and analytical guidelines needed for a successful development and deployment of predictive maintenance solutions. These guidelines are prepared to help the audience create an initial solution using Cortana Analytics Suite and specifically Azure Machine Learning as a starting point in their long term predictive maintenance strategy. The documentation regarding Cortana Analytics Suite and Azure Machine Learning can be found in Cortana Analytics and Azure Machine Learning pages.

Playbook Overview and Target Audience

This playbook is organized to benefit both technical and non-technical audience with varying backgrounds and interests in predictive maintenance space. The playbook covers both high level aspects of the different types of predictive maintenance solutions and details of how to implement them. The content is balanced to cater both to the audience who are only interested in understanding the solution space and the type of applications as well as those who are looking to implement these solutions and are hence interested in the technical details.

Majority of the content in this playbook does not assume prior data science knowledge or expertise. However, some parts of the playbook will require somewhat familiarity with data science concepts to be able to follow implementation details. Introductory level data science skills are required to fully benefit from the material in those sections.

The first half of the playbook covers an introduction to predictive maintenance applications, how to qualify a predictive maintenance solution, a collection of common use cases with the details of the business problem, the data surrounding these use cases and the business benefits of implementing these predictive maintenance solutions. These sections don’t require any technical knowledge in the predictive analytics domain.

In the second half of the playbook, we cover the types of predictive modeling techniques for predictive maintenance applications and how to implement these models through examples from the use cases outlined in the first half of the playbook. This is illustrated by going through the steps of data preprocessing such as data labeling and feature engineering, model selection, training/testing and performance evaluation best practices. These sections are suitable for technical audience. View more