Takes into account loss function specially designed to detect week signals.
Picks up on signs of degradation from faint partial discharges arising in power sources and other facilities.
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)
Please include the title “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.
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.