改进的LSTM方法在冷水机组传感器故障检测中的应用

doi:10.19595/j.cnki.1000-6753.tces.180637

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Abstract At present, the sensor deviation fault detection of the water-cooled chiller is unexpected all over the world. Long short-term memory (LSTM) is suitable for processing high-dimensional, high-coupling, high-time correlation data. Taking into account the characteristics of LSTM, this paper proposed a deep learning method based on improved LSTM for the sensor deviation fault detection of the water-cooled chiller. The paper collected sensor data of the water-cooled chiller on site to train the improved LSTM network. It could be know through simulation experiments that the detection efficiency of different sensors is different, and the results were compared with the results of three other methods: auto encoder, principal component analysis (PCA) and standard LSTM. Finally, the detection efficiency of the method proposed in this paper is significantly better than the other three methods in sensor deviation fault detection of the water-cooled chiller. Furthermore, the detection efficiency of the proposed method has better symmetry for positive and negative fault levels with same absolute magnitude. Finally, it was proved that the proposed network has good generalization ability.

Key words： Long short-term memory deep learning water-cooled chiller sensor fault detection

Received: 19 April 2018 Published: 14 June 2019

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TP277

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	Li Donghui
	Yin Haiyan
	Zheng Bowen
	Liu Lingling

Cite this article:

Li Donghui,Yin Haiyan,Zheng Bowen等. Application of Improved LSTM Method in Sensor Fault Detection of the Chiller[J]. Transactions of China Electrotechnical Society, 2019, 34(11): 2324-2332.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.180637 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I11/2324

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