Fault diagnosis of chiller based on whale optimization algorithm to optimize long and short-term memory network

Jing Qiu; Hua Zhang; Hongxian Zhang; Mengqi Zhou

doi:10.1117/12.2634919

25 April 2022 Fault diagnosis of chiller based on whale optimization algorithm to optimize long and short-term memory network

Jing Qiu, Hua Zhang, Hongxian Zhang, Mengqi Zhou

Author Affiliations +

Proceedings Volume 12244, 2nd International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2022); 122443R (2022) https://doi.org/10.1117/12.2634919
Event: 2nd International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2022), 2022, Guilin, China

Abstract

The failure of water chiller will cause a series of problems such as increasing energy consumption, decreasing comfort of users and decreasing life of equipment. The fault diagnosis of the chiller has an important role in the stable operation of the air conditioner. In order to improve the fault diagnosis level of water chillers and solve the problems of difficulty in determining the parameters of neural network and great randomness, this paper presents a fault diagnosis method of long short-term memory (LSTM) network based on whale optimization algorithm (WOA) optimization. The fault diagnosis performance was verified by RP-1043 dataset, and compared with the results of recurrent neural network (RNN) and BP neural network. The results show that the WOA-LSTM method can effectively identify five typical chillers, and the diagnostic accuracy is as high as 99.27%. Compared with RNN and BP neural network, the proposed method is helpful to detect faults as early as possible and reduce the loss. It is especially obvious to improve the detection efficiency of small faults and complex faults.

Citation Download Citation

Jing Qiu, Hua Zhang, Hongxian Zhang, and Mengqi Zhou "Fault diagnosis of chiller based on whale optimization algorithm to optimize long and short-term memory network", Proc. SPIE 12244, 2nd International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2022), 122443R (25 April 2022); https://doi.org/10.1117/12.2634919

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