TY - GEN
T1 - A novel intelligent faults diagnosis approach based on Ada-REIELM and its application to complex chemical processes
AU - Xu, Yuan
AU - Jiang, Xue
AU - Zhang, Mingqing
AU - He, Yanlin
AU - Duan, Fang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - In this paper, a novel fault diagnosis method integrating a recurrent error incremental extreme learning machine (REIELM) with Adaptive Boosting (AdaBoost) is proposed. EIELM can adaptively select the number of neurons by adding them one by one. For further improving the performance of EIELM, a feedback layer is added between the output layer and the hidden layer for remembering the outputs of hidden layer, and the trend change rate is computed to dynamically update the feedback layer outputs. In addition, as the features of input data have impact on the diagnosis results, AdaBoost algorithm is used to adjust the weights of the output in the training process of REIELM, so that the optimal parameters are obtained. To verify the performance of the proposed method, standard UCI data sets and TE simulation process are selected. Simulation results show that the proposed method achieves better performances in fault diagnosis than traditional approaches.
AB - In this paper, a novel fault diagnosis method integrating a recurrent error incremental extreme learning machine (REIELM) with Adaptive Boosting (AdaBoost) is proposed. EIELM can adaptively select the number of neurons by adding them one by one. For further improving the performance of EIELM, a feedback layer is added between the output layer and the hidden layer for remembering the outputs of hidden layer, and the trend change rate is computed to dynamically update the feedback layer outputs. In addition, as the features of input data have impact on the diagnosis results, AdaBoost algorithm is used to adjust the weights of the output in the training process of REIELM, so that the optimal parameters are obtained. To verify the performance of the proposed method, standard UCI data sets and TE simulation process are selected. Simulation results show that the proposed method achieves better performances in fault diagnosis than traditional approaches.
KW - Adaptive boosting (AdaBoost)
KW - Error increment
KW - Extreme learning machine (ELM)
UR - http://www.scopus.com/inward/record.url?scp=85049788511&partnerID=8YFLogxK
U2 - 10.1109/ICACI.2018.8377523
DO - 10.1109/ICACI.2018.8377523
M3 - Conference contribution
AN - SCOPUS:85049788511
T3 - Proceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
SP - 573
EP - 577
BT - Proceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Advanced Computational Intelligence, ICACI 2018
Y2 - 29 March 2018 through 31 March 2018
ER -