TY - GEN
T1 - An Ensemble Approach for Fault Diagnosis via Continuous Learning
AU - Zhang, Dapeng
AU - Gao, Zhiwei
N1 - Funding Information: The authors would like to thank the research support from the National Nature Science Foundation of China (NNSFC) under Grant 61673074, School of Electrical and Information Engineering at Tianjin University, China, and the E&E faculty at Northumbria University, Newcastle upon Tyne, Tyne and Wear, NE1 8ST, United Kingdom.
PY - 2021/7/21
Y1 - 2021/7/21
N2 - The great success of deep neural network (DNN) in image field stimulates its application in fault detection and diagnose. However due to the limitation of system security, it is impossible to obtain complete fault data as the training database for neural network, so that it is challenging to identify a fault that never occurred before. In this paper, an ensemble approach is proposed to adapt to a new fault by adding output branches of the neural network. Firstly, the time series are transferred to numerous imaging matrixes. The intrinsic characteristics of the matrixes are then extracted using deep neural network which are used to judge whether it is a new fault according to the distance criterion. For a new fault, the DNN will retrain by transferring learning in order to reduce the computation and training time. The effectiveness of the algorithm is demonstrated by a numerical simulation example based on a wind turbine benchmark model.
AB - The great success of deep neural network (DNN) in image field stimulates its application in fault detection and diagnose. However due to the limitation of system security, it is impossible to obtain complete fault data as the training database for neural network, so that it is challenging to identify a fault that never occurred before. In this paper, an ensemble approach is proposed to adapt to a new fault by adding output branches of the neural network. Firstly, the time series are transferred to numerous imaging matrixes. The intrinsic characteristics of the matrixes are then extracted using deep neural network which are used to judge whether it is a new fault according to the distance criterion. For a new fault, the DNN will retrain by transferring learning in order to reduce the computation and training time. The effectiveness of the algorithm is demonstrated by a numerical simulation example based on a wind turbine benchmark model.
KW - Deep learning
KW - Fault detection and diagnose
KW - Time series
KW - Wind turbine benchmark model
UR - http://www.scopus.com/inward/record.url?scp=85125586768&partnerID=8YFLogxK
U2 - 10.1109/INDIN45523.2021.9557388
DO - 10.1109/INDIN45523.2021.9557388
M3 - Conference contribution
AN - SCOPUS:85125586768
SN - 9781728143965
T3 - IEEE International Conference on Industrial Informatics (INDIN)
BT - Proceedings - 2021 IEEE 19th International Conference on Industrial Informatics (INDIN)
PB - IEEE
CY - Piscataway, US
T2 - 19th IEEE International Conference on Industrial Informatics, INDIN 2021
Y2 - 21 July 2021 through 23 July 2021
ER -
