by Exosite, on April 27, 2016
In a connected device deployment, it is important to define what happened and why it happened, in order to predict what will happen. Building from our previous IoT analytics blog segment on diagnostic analytics, this series begins to break down key features of predictive analytics.
Once diagnostic analytics have been used to identify key causal relationships between past IoT-connected device behaviors and events, it is possible to leverage retroactive insights to begin projecting probable device behavior into the future.
The benefits of knowing what an IoT device fleet or external entity is going to do are far-reaching. It allows technicians to schedule proactive maintenance to reduce unexpected downtime, enables organizations to save thousands or millions of dollars in damage caused by mal-performing equipment, and eliminates the potential for degraded brand trust caused by unexpected device behavior.
Making the leap from predicting future device behavior to developing a system that automatically takes action based on those predictions can be complex and often involves extensive algorithms that force device behavior.
Machine learning is a system in which computer apply statistical learning techniques to automatically identify patterns in data, modify those patterns as more data is gathered, and make highly accurate predictions. Machine learning algorithms are trained with control and anomalous data to recognize normal and abnormal device behavior. They can also be retaught when conditions for failure change if, for example, a device upgrade is made.
Machine learning is an incredibly powerful tool for predictive analytics in IoT applications and has massive efficiency and performance ramifications for manufacturers. With mature machine learning analytics in place, manufacturers can limit downtime and liabilities as their system predicts and self-diagnoses failure and adapts to new device conditions to operate effectively regardless of context. Although there are many machine learning techniques in use today, Exosite has noted three key concepts in our Data Analytics for IoT white paper, including:
Cluster Analysis
Classification
Random Forest
For a complete description of our key concepts, download the full white paper or contact us to kick-start your IoT solution.
