IoT data analysis model
Analysis of IoT data in the logistic industry
This is an IoT solution in combination with machine learning algorithm for the logistic industry, aiming at analyzing daily behaviors of the forklifts. Those classified behavior data can be use by other IoT applications for further analysis, for example analysis functions of RIOTANA (Real-time IoT Analytics) by Fraunhofer ISST. We collect real motion sensor data from forklifts and develop machine learning algorithms to recognize the behaviors.
Format: Python
Execution Environment: Linux, Windows, OSX
Additional information: In the logistic industry, IoT applications are considered as cheap solutions for variety of use cases, and could be easily combined with machine learning and artificial intelligent in order to develop smart maintenance and supervision models for the industrial assets. Especially, in the contact with cooperation partners in the logistic industry, we found that the behavior data of forklifts is full of value, which not only indicates directly the working status of a forklift (e.g. loading, unloading, normal driving, harmful vibration etc.), but is also possible to be further used for analyzing high level KPIs. For instance, the analyzing of forklift workload is valuable to arrange assets and forecast demand, as well as for predictive maintenance.