Convolutional neural networks-based valve internal leakage recognition model

The internal leakage signal of a valve is generally weak, and it is vulnerable to complex background noise. Due to the limitations of traditional internal leakage diagnosis methods and models, it is difficult to effectively evaluate a valve state under complex working conditions. Recognising this challenge, convolutional neural networks (CNN) is proposed to recognise valve internal leakage, which uses the power spectral density images of internal leakage and non-leakage signals under multiple working conditions as input. The experimental results show that the proposed models effectively recognise a internal leakage or non-leakage signal, and the maximum prediction error is less than 3%, which can be used a new method for valve leakage diagnosis.

Automated summary

The traditional methods for diagnosing valve internal leakage have limitations, leading to the proposal of a new method using convolutional neural networks to recognize valve internal leakage. Experimental results show that this method can effectively identify internal leakage signals, with a maximum prediction error of less than 3%, serving as a new approach for valve leakage diagnosis.