With the advent of Internet of Things (IOT) and the proliferation of connected devices, comes the challenge of monitoring parts for maintenance before they break down. A common approach revolves around getting data from connected devices and performing a statistical test to determine the likelihood of the device failing. While this common approach is robust, it typically involves a significant time investment in exploratory data analysis, feature engineering, training, and testing to build a predictive model. It, therefore, often lacks the agility required to keep up with the monitoring demands of increasingly time-sensitive initiatives.
In this context, the question becomes: how can we ensure a similar degree of rigor, but also improve the timeliness and responsiveness of being able to perform predictive maintenance?
Click through for the process, as well as an example using Azure Stream Analytics and Power BI.