Prediction method for compressor real energy head characteristics based on deep learning network

The operating characteristics of the compressor may differ from the factory test characteristics. In order to guide the safe and stable operation of the compressor, a prediction model of compressor real energy head characteristics based on deep learning network was established considering the calculation method of compressor characteristics and some real characteristics. A large number of real energy head data of compressors under different working conditions was used as training samples of the deep learning network, and after training, the model precision was checked by untrained samples, concluding the maximum relative error of 2.60%, the minimum relative error of 0.32% and the average relative error of 0.78%. The real energy head curve drawn by the deep learning network is well consistent with the actual energy head curve. The established deep learning network model improves the defects of the traditional neural network, with good prediction accuracy and generalization calculation ability, providing a new method for compressor performance evaluation and prediction.