Analysis of Gas Leakage Early Warning System Based on Kalman Filter and Optimized BP Neural Network

Abstract

This paper proposes a method for gas leakage early warning system based on Kalman filter and back-propagation (BP) neural network to address the issue of inaccurate gas leakage detection and incapability of predicting concentration change of gas. First, Kalman filter is adopted to filter the noise from the gas concentration that is measured by a sensor. Then, predictions about the change of gas concentration are made using the BP neural network that is optimized by genetic algorithm. Next, the gas leakage early warning system, based on the proposed method, is designed. Last, to verify the effectiveness of the method proposed by simulation, methane, the main component of gas is chosen as an example. Also introduced in this paper are the determinant coefficient, mean absolute error, correlation coefficient and root-mean-square error ─ the four evaluation indicators methods to demonstrate the effectiveness and feasibility of the algorithm this paper proposed by comparing with Support Vector Machine (SVM), Long Short-term Memory (LSTM) and general Back Propagation Neural Network (BPNN). The best validation performance of BP neural network through simulation experiments and is 0.013518, and the probability of the relative error between the predicted value and the actual value within 10% is 0.7692. The proposed method can effectively improve the accuracy of gas concentration prediction as comparison results show, and it has advantage in fitting degree and error fluctuations.