TY - GEN
T1 - Initial Attempt on Wi-Fi CSI Based Vibration Sensing for Factory Equipment Fault Detection
AU - Jian, Sirui
AU - Ishida, Shigemi
AU - Arakawa, Yutaka
N1 - Funding Information:
This work is supported in part by JSPS KAKENHI Grant Numbers JP18K18041 and JP19KK0257.
PY - 2021/1/5
Y1 - 2021/1/5
N2 - Wi-Fi signal based detection is widely implemented in indoor action detection because of its low-cost and easy implementation. But it is still rarely used in equipment vibration detection. Moreover, it is hard to detect multiple targets where we need to monitor multiple equipments' vibration state such as in the factory environment. In this paper, we propose a wireless based vibration sensing method using Wi-Fi for factory equipment fault detection. First, we use CSI amplitude data to distinguish sensing target equipments. Then, we apply an anomaly detection method to detect faulty machine operation. We conducted initial experiments to validate the feasibility of our proposed fault detection method. The experimental results show that our method detected abnormal situations with an accuracy of 100%, while 10% of normal situations were mistakenly recognized as abnormal.
AB - Wi-Fi signal based detection is widely implemented in indoor action detection because of its low-cost and easy implementation. But it is still rarely used in equipment vibration detection. Moreover, it is hard to detect multiple targets where we need to monitor multiple equipments' vibration state such as in the factory environment. In this paper, we propose a wireless based vibration sensing method using Wi-Fi for factory equipment fault detection. First, we use CSI amplitude data to distinguish sensing target equipments. Then, we apply an anomaly detection method to detect faulty machine operation. We conducted initial experiments to validate the feasibility of our proposed fault detection method. The experimental results show that our method detected abnormal situations with an accuracy of 100%, while 10% of normal situations were mistakenly recognized as abnormal.
UR - http://www.scopus.com/inward/record.url?scp=85098886814&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098886814&partnerID=8YFLogxK
U2 - 10.1145/3427477.3429462
DO - 10.1145/3427477.3429462
M3 - Conference contribution
AN - SCOPUS:85098886814
T3 - ACM International Conference Proceeding Series
SP - 163
EP - 168
BT - ICDCN 2021 - Adjunct Proceedings of the 2021 International Conference on Distributed Computing and Networking
PB - Association for Computing Machinery
T2 - 22nd International Conference on Distributed Computing and Networking, ICDCN 2021
Y2 - 5 January 2021 through 8 January 2021
ER -