TY - GEN
T1 - Data-driven condition monitoring of control valves in laboratory test runs
AU - Weis, Iris
AU - Hanel, Andreas
AU - Trunzer, Emanuel
AU - Pirehgalin, Mina Fahimi
AU - Unland, Stefan
AU - Vogel-Heuser, Birgit
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The availability of huge amounts of process data enables data-driven methods to optimize production processes. Predictive Maintenance is one of the common applications to transfer data to useful information for improving the Overall Equipment Effectiveness. In this paper, a data-driven method for condition monitoring of control valves in industrial process plants is developed based on data collected during test runs. In contrast to the state of the art condition monitoring in control valves, the test runs make it possible to define a threshold that differentiates normal from abnormal valve behaviour. Furthermore, the characteristics of the model results allow the identification of different defects. The application of the proposed method to a historic industrial data set validate the applicability in noisy industrial use cases.
AB - The availability of huge amounts of process data enables data-driven methods to optimize production processes. Predictive Maintenance is one of the common applications to transfer data to useful information for improving the Overall Equipment Effectiveness. In this paper, a data-driven method for condition monitoring of control valves in industrial process plants is developed based on data collected during test runs. In contrast to the state of the art condition monitoring in control valves, the test runs make it possible to define a threshold that differentiates normal from abnormal valve behaviour. Furthermore, the characteristics of the model results allow the identification of different defects. The application of the proposed method to a historic industrial data set validate the applicability in noisy industrial use cases.
KW - Anomaly detection
KW - Control valves
KW - Fault identification
UR - http://www.scopus.com/inward/record.url?scp=85079040017&partnerID=8YFLogxK
U2 - 10.1109/INDIN41052.2019.8972328
DO - 10.1109/INDIN41052.2019.8972328
M3 - Conference contribution
AN - SCOPUS:85079040017
T3 - IEEE International Conference on Industrial Informatics (INDIN)
SP - 1291
EP - 1296
BT - Proceedings - 2019 IEEE 17th International Conference on Industrial Informatics, INDIN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Industrial Informatics, INDIN 2019
Y2 - 22 July 2019 through 25 July 2019
ER -