畳み込みニューラルネットワークに基づく風車異常検知システムにおける判断根拠の可視化に関する検討

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pcl-lab

佐伯真於，緒方淳，村川正宏，小川哲司，``畳み込みニューラルネットワークに基づく風車異常検知システムにおける判断根拠の可視化に関する検討，'' 第40回風力エネルギー利用シンポジウム，Dec. 2018.

Engineering

2
[Andersen et al., 2017]
[Hasegawa et al., 2018]

(Gaussou et al. 2017)
BSF 278 Hz
FTF 15 Hz
BPFO 236 Hz
BSF
FTF BPFO n
12
IMS
• BSF: Ball spin frequency
• FTF: Fundamental train frequency
• BPFO: Ball passing frequency outer race

Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control. In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.

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畳み込みニューラルネットワークに基づく風車異常検知システムにおける判断根拠の可視化に関する検討

1. NEDO

2. ( ) 1 ü ü

3. 2 [Andersen et al., 2017] [Hasegawa et al., 2018]

4. 3 Hz

5. 4

6. CNN 5 CNN CNN

7. 6 Grad-CAM (Selvaraju et al., 2017)

8. 7 Grad-CAM

9. 8 Grad-CAM

10. 9 Grad-CAM

11. 10 Grad-CAM (Selvaraju et al., 2017)

12. 11 Grad-cam Grad-cam Grad-cam

13. (Gaussou et al. 2017) BSF 278 Hz FTF 15 Hz BPFO 236 Hz BSF FTF BPFO n 12 IMS • BSF: Ball spin frequency • FTF: Fundamental train frequency • BPFO: Ball passing frequency outer race

14. Grad-CAM BSF 6 FTF 13

15. BPFO 14Grad-CAM

16. CMS 15

17. 16 CNN

18. 17 N N A C D S N

19. 18

20. 19

21. 20 xBPSO +FTF xBPSO +FTF