TY - GEN
T1 - Integrated Group-based Valuable Sensor Selection Approach for Remaining Machinery Life Estimation in the Future Industry 4.0 Era
AU - Chen, Kun Chih Jimmy
AU - Gao, Zi Jie
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - Industry 4.0 is the evolution trend for current manufacturing technology. By analyzing the real-Time sensing data, the health status of each machinery is usually monitored to reduce the risk of suddenly machine failure. Although massive sensors allocation can leverage the Remaining Useful Life (RUL) estimation for each machinery, the cost for the sensor network construction will become expensive. Hence, it is necessary to have an approach to remove the redundant sensors under a certain constraint of RUL estimation. On the other hand, due to the attractive performance on the object classification, many researches apply Artificial Neural Network (ANN) to decide which allocated sensor should be removed during the training process. However, the current researches aim to remove the redundant sensors based on the sensing data at a specific time, which lacks the intrinsic feature of time-series sensing data. Therefore, the current researches suffer from the problem of sensor under-killing due to the worst-case consideration. In this paper, we consider the information of time-series sensing data to propose an integrated group-based valuable sensor selection algorithm. Because the proposed approach considers the historical data during the redundant sensor removing process, we can reduce the number of involved allocated sensors precisely and significantly. In order to verify the proposed method, we use the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset and adopt Prognostics and Health Management (PHM) score to evaluate the RUL estimation performance. Compared with the conventional approach, the proposed approach can reduce 86% average PHM score and employ fewer sensors to fit the strict constraint of PHM score with less computing overhead.
AB - Industry 4.0 is the evolution trend for current manufacturing technology. By analyzing the real-Time sensing data, the health status of each machinery is usually monitored to reduce the risk of suddenly machine failure. Although massive sensors allocation can leverage the Remaining Useful Life (RUL) estimation for each machinery, the cost for the sensor network construction will become expensive. Hence, it is necessary to have an approach to remove the redundant sensors under a certain constraint of RUL estimation. On the other hand, due to the attractive performance on the object classification, many researches apply Artificial Neural Network (ANN) to decide which allocated sensor should be removed during the training process. However, the current researches aim to remove the redundant sensors based on the sensing data at a specific time, which lacks the intrinsic feature of time-series sensing data. Therefore, the current researches suffer from the problem of sensor under-killing due to the worst-case consideration. In this paper, we consider the information of time-series sensing data to propose an integrated group-based valuable sensor selection algorithm. Because the proposed approach considers the historical data during the redundant sensor removing process, we can reduce the number of involved allocated sensors precisely and significantly. In order to verify the proposed method, we use the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset and adopt Prognostics and Health Management (PHM) score to evaluate the RUL estimation performance. Compared with the conventional approach, the proposed approach can reduce 86% average PHM score and employ fewer sensors to fit the strict constraint of PHM score with less computing overhead.
UR - http://www.scopus.com/inward/record.url?scp=85093694039&partnerID=8YFLogxK
U2 - 10.1109/VLSI-DAT49148.2020.9196260
DO - 10.1109/VLSI-DAT49148.2020.9196260
M3 - Conference contribution
AN - SCOPUS:85093694039
T3 - 2020 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2020
BT - 2020 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Symposium on VLSI Design, Automation and Test, VLSI-DAT 2020
Y2 - 10 August 2020 through 13 August 2020
ER -