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.
Highlights
Gas leakage accidents have become frequent occurrences with the popularization of piped gas and the expansion of gas supply systems
MODEL ANALYSIS AND DISCUSSION We focus on the comparison of Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), Long Short-term Memory (LSTM) algorithm in order to demonstrate the effectiveness of Kalman filter (KF)-BP-genetic algorithm (GEN) NNS algorithm as proposed
The KF-BP-GEN NNS this paper proposed has the best goodness of fit among of the SVM, BPNN, LSTM and KF-BP-GEN NNS; and the KF-BP-GEN NNS this paper proposed has the good degree of closeness between the predicted and the measured values, and the absolute error of predicted and measured values has the minimum degree of fluctuation
Summary
Gas leakage accidents have become frequent occurrences with the popularization of piped gas and the expansion of gas supply systems. This paper, based on the deep learning framework proposes to use the neural network (BP) based on Kalman filter (KF), and the optimization method using genetic algorithm (GEN) to achieve the prediction of leakage as the above research shows that the performance of the algorithm under the deep learning framework is better than other prediction algorithms. In the end, calculating the best weights and thresholds provides feedback to BP neural networks
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