TY - GEN
T1 - Failure mode classification for control valves for supporting data-driven fault detection
AU - Trunzer, Emanuel
AU - Weis, Iris
AU - Folmer, Jens
AU - Schrufer, Carolin
AU - Vogel-Heuser, Birgit
AU - Erben, Stefan
AU - Unland, Stefan
AU - Vermum, Christian
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Significant losses of production due to unplanned downtimes are a major problem caused by technical failures of equipment. Existing approaches like failure mode and effect analysis try to identify possible equipment breakdowns, their causes and effects in order to quantify the reliability of the system. Yet, they are not used for the detection of faults. On the other hand, Industrie 4.0 and data mining aim to improve the total operating time of automated production systems. However, due to the complexity of automated production systems and the underlying physical phenomena, it is essential to formalize expert knowledge for usage during data analysis. In this contribution a classification table is proposed, in which the expert knowledge on failure modes, underlying parameters and detection features are summarized and presented. This knowledge is used to formulate appropriate detection models. The evaluation for detection of failure modes for control valves showed the usefulness of combination of expert knowledge and data-driven data analysis.
AB - Significant losses of production due to unplanned downtimes are a major problem caused by technical failures of equipment. Existing approaches like failure mode and effect analysis try to identify possible equipment breakdowns, their causes and effects in order to quantify the reliability of the system. Yet, they are not used for the detection of faults. On the other hand, Industrie 4.0 and data mining aim to improve the total operating time of automated production systems. However, due to the complexity of automated production systems and the underlying physical phenomena, it is essential to formalize expert knowledge for usage during data analysis. In this contribution a classification table is proposed, in which the expert knowledge on failure modes, underlying parameters and detection features are summarized and presented. This knowledge is used to formulate appropriate detection models. The evaluation for detection of failure modes for control valves showed the usefulness of combination of expert knowledge and data-driven data analysis.
UR - http://www.scopus.com/inward/record.url?scp=85045234597&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2017.8290311
DO - 10.1109/IEEM.2017.8290311
M3 - Conference contribution
AN - SCOPUS:85045234597
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 2346
EP - 2350
BT - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
PB - IEEE Computer Society
T2 - 2017 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2017
Y2 - 10 December 2017 through 13 December 2017
ER -