Machine Learning and Internet of Things

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The data collected from IoT devices over time is enormous and would be difficult for one person or even a team to uncover all insights. That is where machine learning comes in – it can find patterns in data, it can scale and simplify IoT data analysis. Internet of Things and Machine Learning complement each other.

Using IoT, we can often end up collecting massive amounts of data. For the human eye to make sense of it becomes a real challenge. Machine Learning is used, in conjunction with IoT, when traditional data analysis and mathematical models are not enough to translate data into actionable insights. This can be termed as automation of analytics.

As the connections and interconnectivity grow, a massive amount of data will be produced. Stored in the cloud, IoT will work better with machine learning, and will only get better. The system, through machine learning, will be able to identify and rectify problems even before the users will. Warnings about any malfunctioning device (see IoT section) can be given out before the defective pieces affect the entire system.

IoT and ML can be effectively implemented in machine prognostics — an engineering discipline that mainly focuses on predicting the time at which a system or component will no longer perform its intended function. So, ML with IoT can be effectively implemented in system health management (SHM), e.g., in transportation applications, in vehicle health management (VHM) or engine health management (EHM).

Thanks to Industrial IoT and Machine Learning, we are now entering ‘Predictive Maintenance’ from Reactive Maintenance and Routine Maintenance.