The Industrial Internet Consortium (IIC) announced results for the Deep Learning Facility Testbed. IIC-member participants Dell EMC and Toshiba developed an application to explore deep learning through neural networks within an IoT platform to optimize asset utilization in an office building.
The Deep Learning Facility Testbed, located in a Toshiba facility in Kawasaki, Japan, analyzes 35,000 measured data points per minute, working to optimize the maintenance of monitored assets of the building. With this amount of data collected, the testbed relies on artificial intelligence to detect anomalies in order to improve the visitor experience with things like prioritized elevator scheduling and automated temperature and lighting controls.
The structure of buildings varies, and it’s very costly to design an anomaly detection system to fit the building. The testbed application learns what a normal condition would be using data aggregated from many sensors installed in the facility. The team has been able to use the data to determine an unusual condition, locate the suspected device and let staff check if the inference is correct. For example, the testbed detected the unusual state of the air conditioning equipment in the kitchen, and building facility management staff found out that the air intake ducts in the kitchen had been closed to avoid odor by kitchen staff.