In order to solve the problems of low accuracy and low efficiency of traditional sensor data anomaly diagnosis methods, a new temperature sensor data anomaly diagnosis method based on deep neural network is proposed in this paper. Firstly, the temperature sensor data in a running cycle is collected, and the characteristics of the temperature sensor data are extracted by the sliding window technology. Secondly, based on the feature extraction results, a deep neural network model for anomaly diagnosis of temperature sensor data is constructed. The feature data are input into the model, and the result obtained is the diagnosis result. Finally, the simulation comparison experiment is carried out. The experimental results show that the error rate of feature extraction of temperature sensor data in this method changes between −2.1% and 5.9%, the diagnosis accuracy remains above 95%, the average diagnosis time is only 59 ms, and the diagnosis efficiency is high.
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