An Architecture for Managing Faulty Sensing Data on Low Cost Sensing Devices over Manufacturing Equipments

KIPS Transactions on Software and Data Engineering, Vol. 7, No.3, pp.113-120, March 2018
10.3745/KTSDE.2018.7.3.113, Full Text

Abstract

In this study, we proposed a monitoring system for identifying and handling faulty sensing stream data on manufacturing equipments where low-cost sensors can be safely used. Low cost sensors will lessen the cost of implementing distributed monitoring system, but suffer from sensor noises and inaccurate sensed data. Therefore, a distributed monitoring system with low cost sensors should identify faulty signal data as either of sensor fault or machine fault, and filter out faulty signals from sensing fault. To this end, we adopted a fourier transform based diagnostic approach mixed with a weighed moving averaging method, in order to identify faulty signals. We measured how effective our approach is and found out our approach can filter out one-third faulty signals from our experimental environment. In addition, we attached wireless communication modules to reduce sensor and network installation cost. To handle massive sensor data efficiently, we employed unstructured data format with NoSQL based database.


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Cite this paper

[KIPS Transactions Style]
Y. Chae, C. Kim, H. Ko, and W. Kim, "An Architecture for Managing Faulty Sensing Data on Low Cost Sensing Devices over Manufacturing Equipments," KIPS Transactions on Software and Data Engineering, Vol.7, No.3, pp.113-120, 2018, DOI: 10.3745/KTSDE.2018.7.3.113.

[IEEE Style]
Yuna Chae, Changi Kim, Haram Ko, and Woongsup Kim, "An Architecture for Managing Faulty Sensing Data on Low Cost Sensing Devices over Manufacturing Equipments," KIPS Transactions on Software and Data Engineering, vol. 7, no. 3, pp. 113-120, 2018. DOI: 10.3745/KTSDE.2018.7.3.113.

[ACM Style]
Chae, Y., Kim, C., Ko, H., and Kim, W. 2018. An Architecture for Managing Faulty Sensing Data on Low Cost Sensing Devices over Manufacturing Equipments. KIPS Transactions on Software and Data Engineering, 7, 3, (2018), 113-120. DOI: 10.3745/KTSDE.2018.7.3.113.