WANG Luyao, WU Bin, DU Zhimin, et al. Sensor fault detection and diagnosis for data center air conditioning system based on LSTM neural network[J]. CIESC Journal, 2018, (S2): 252-259.
WANG Luyao, WU Bin, DU Zhimin, et al. Sensor fault detection and diagnosis for data center air conditioning system based on LSTM neural network[J]. CIESC Journal, 2018, (S2): 252-259.DOI: 10.11949/j.issn.0438-1157.20181084.
The faults of data center air-conditioning system directly results in increasing energy consumption and reducing operation reliability. Combined with deep learning technology
a fault detection and diagnosis method of sensors for air conditioning system is proposed by using LSTM (long short term memory) neural network. Verified by the experiment
the method can identify the fixed biases and drifting biases of both liquid line temperature sensor and discharge temperature sensor through building the fault detection and diagnosis models for them respectively. For the fault-free condition
the detection accuracy of this method is about 90%. For the sensor fault whose deviation degree is greater than the minimum detection deviation of the method