TY - JOUR
T1 - A Multimode Anomaly Detection Method Based on OC-ELM for Aircraft Engine System
AU - Chen, Shaowei
AU - Wu, Meng
AU - Wen, Pengfei
AU - Xu, Fangda
AU - Wang, Shengyue
AU - Zhao, Shuai
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - The practical industrial processes possess the characteristics of multimode, unbalanced data distribution, and complex types of abnormalities, which are challenging to the anomaly detection task of complex industrial systems. In this paper, a novel anomaly detection framework based on one-class extreme learning machine (OC-ELM) for the multimode system is presented. To tackle the multiple operation modes, a clustering algorithm is first applied to distinguish the operation modes of the system. The corresponding detection models are built under different operation modes resulting in the multiple models operated in parallel. In addition, the proposed method constructs the reasonable boundary of the complex data distribution, reflecting the equipment running in the healthy or the normal state. The anomaly detection index is obtained according to the deviation degree between the testing sample and the normal model. As a result, a global monitoring index reflecting the degradation state is obtained by combining the anomaly monitoring indices of the equipment under multiple operation modes. The proposed method is verified on a public dataset of aircraft engines, and the advantages are demonstrated by comparing with the implemented detection model without handling the information of operation modes, and the multiple principal component analysis method.
AB - The practical industrial processes possess the characteristics of multimode, unbalanced data distribution, and complex types of abnormalities, which are challenging to the anomaly detection task of complex industrial systems. In this paper, a novel anomaly detection framework based on one-class extreme learning machine (OC-ELM) for the multimode system is presented. To tackle the multiple operation modes, a clustering algorithm is first applied to distinguish the operation modes of the system. The corresponding detection models are built under different operation modes resulting in the multiple models operated in parallel. In addition, the proposed method constructs the reasonable boundary of the complex data distribution, reflecting the equipment running in the healthy or the normal state. The anomaly detection index is obtained according to the deviation degree between the testing sample and the normal model. As a result, a global monitoring index reflecting the degradation state is obtained by combining the anomaly monitoring indices of the equipment under multiple operation modes. The proposed method is verified on a public dataset of aircraft engines, and the advantages are demonstrated by comparing with the implemented detection model without handling the information of operation modes, and the multiple principal component analysis method.
KW - Aircraft engine system
KW - anomaly detection
KW - global monitoring index
KW - multiple operation modes
KW - OC-ELM
UR - http://www.scopus.com/inward/record.url?scp=85101471064&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3057795
DO - 10.1109/ACCESS.2021.3057795
M3 - Journal article
AN - SCOPUS:85101471064
SN - 2169-3536
VL - 9
SP - 28842
EP - 28855
JO - IEEE Access
JF - IEEE Access
M1 - 9349499
ER -