When parts fail, the resulting costs can be significant as it can result in equipment damage, downtime or even danger to workers or customers.
- Using vibration and temperature sensors, failure of the auxiliary power unit on an aircraft was predicted
- Model was trained to identify nominal behavior – when the APU started deviating from the nominal behavior, investigations revealed imminent failure
How to Customize
- Sensor and event data for equipment or system
- Applicable to failure prediction of parts or systems
We were able to successfully train an AI model to recognize complex industrial parts using Neopulse 3.0 on AWS. The AI solution was built very quickly and was able to recognize objects in unpredictable, real-world environments with high accuracy.
Everybody seems confident in AI, and they actually enjoy solving various AI problems.
The vendor’s services are integral to providing AI solutions for a wider audience. They had an effective project management style, accented by a quick working style.