AI-based contactless temperature sensor anomaly detection simulation demo
The AI will learn and infer, displaying the degree of anomaly associated with temperature changes.
The temperature data collected by a non-contact temperature sensor (thermal watcher) will be used for AI learning inference in the Solist-AI TM simulator to display the degree of abnormality associated with temperature changes. The Solist-AI TM simulator is software that simulates the AI accelerator function of Solist-AI TMIC (ML63Q2557) on Windows. - A demo device mimicking a control panel is created in a normal state (initial powered state) and a load state, and temperature data from terminal blocks and other components is collected using the thermal watcher (32x32 pixel non-contact temperature sensor). - The temperature data from the normal state is used to train the AI in the simulator. - The temperature data from the load state is inferred by the AI in the simulator, and the degree of abnormality associated with temperature changes is displayed in a graph.
basic information
【Specifications (Excerpt)】 <DT-EBML63Q2557 Features> ■ Expanded FRAM: 256KByte ■ Analog Input ・ IEPE power supply available ・ 10kHz 3rd order Butterworth filter included ・ Connected internally to the CPU's A/D converter * For more details, please download the PDF or feel free to contact us.
Price range
Delivery Time
Model number/Brand name
DT-EBML63Q2557
Applications/Examples of results
For more details, please download the PDF or feel free to contact us.
