A non-contacting leak fault diagnosis method for subsea Christmas tree valve based on deep neural network with skip connections

Due to the complex and diverse underwater environment, underwater acoustic transmission is easily disturbed, absorbing many noise components. To deal with the complex noise components is a challenge with the tradi-tional signal processing method. Moreover, it is hard to diagnose the valve's leakage and the leakage degree in the subsea Christmas tree using underwater acoustic signals. This paper proposes a new non-contacting fault diagnosis method based on a deep neural network (DNN) with skip connections. First, we obtain the required time-domain data using acoustic sensors (a digital hydrophone) in experiments. Second, the time-domain data is preprocessed and is converted by the short-time Fourier transform into time-frequency domain signals. The time -frequency domain signals are then input into a constructed DNN model to minimize the noise components in the signal. After that, the denoised data are applied to a convolutional neural network for fault diagnosis. Finally, an underwater acoustic experiment is designed and performed to validate the proposed method's effectiveness. The experimental results demonstrate that our proposed method can effectively diagnose the leakage fault of the valve, and the diagnosis accuracy reaches 98.89%.

Automated summary

Underwater acoustic transmission is easily disturbed and absorbs many noise components due to the complex and diverse underwater environment. Traditional signal processing methods face challenges in dealing with these complex noise components. This paper proposes a new non-contacting fault diagnosis method using a deep neural network (DNN) with skip connections. The effectiveness of the proposed method is validated through an underwater acoustic experiment, achieving a diagnosis accuracy of 98.89%.