- Anomaly detection
- Sensor data recognition
Deep learning denoising
Applications
- Degradation diagnosis for equipments and facilities
Benchmarks, strengths, and track record
- Estimate partial discharge type with 80% accuracy, even in the case of sensor signals that contain noise with amplitude of 10x the partial discharge pulse. (Institute of Electrical Engineers of Japan National Conference 2019)
Inquiries
Please include the title “Toshiba AI Technology Catalog: Deep learning denoising” or the URL in the inquiry text.
Please note that because this technology is currently the subject of R&D activities, immediate responses to inquiries may not be possible.
References:
- Tenta Sasaya et.al.; “Development of switchgear insulation diagnosis technology using AI (3): De-noise technology for detecting partial discharge”; Institute of Electrical Engineers of Japan National Conference 2019. (in Japanese)
- T. Sasaya, et al., “Partial Discharge Classification from Highly Noise-contaminated Data Using Cascaded Two Neural Networks”, 8th International Conference on Condition Monitoring and Diagnosis (CMD), 2020.
