Research On Multi-Source Data Fusion Method And Key Technologies Of Gas Pipeline Leakage Monitoring Network

Natural gas pipeline is one of the important lifelines in the city. Considering the problems of pipeline's deterioration, aging, natural disasters, construction damage and so on, the leakage and the resulted explosion accidents occur frequently, which seriously threaten the security of gas supply system. However, it is difficult for the existed leakage detection methods to identify small and multi-sources leakage accurately. By using wireless sensor networks, it can achieve on-line and real-time security monitoring of gas supply system, and solve the problem of low efficiency of manual inspection.For the effects of measurement noise, sensor types, node numbers and monitoring location, the information shows the form of uncertainties, diversities, enormous quantity and complex relationship. In order to identify the pipeline leakage timely and accurately, such problems should be resolved: (a) removing noise from leakage signal, (b) dealing with the relevance of information from heterogeneous sensors, (c) making united decisions with multi-nodes' diagnosis results.Therefore, as viewed from information fusion, this thesis systematically studies the method of multi-source data processing in pipeline monitoring network. The main work is provided as follows:Firstly, by analysis of the network structure and data characteristics, a hierarchical multi-source leakage monitoring data fusion model based on wavelet neural network and D-S evidence theory is established. At sensor node, wavelet neural network is used for data-level preprocessing and feature-level fusion. Then, at the cluster head, the united decision was made to the orignial multi-nodes' diagnosis results using improved D-S evidence theory. Secondly, considering that the noise interference of leakage detecting signal is very strong under urban environment, the symlets wavelet and heuristic wavelet threshold method are selected for wavelet decomposition and signal-to-noise separation respectively. And the leakage sensitive characteristics of time-frequency domain can be extracted from wavelet decomposition signal. In order to improve the precision of leakage location, a multi-nodes location algorithm is proposed. The algorithm divides all signals into two groups according to the average amplitude of single-state acoustic emission signal. The leak position can be obtained from each pair of signal using waveform cross-correlation method, and then processes it with weighted average. The experiment result shows that the proposed method improves the precision of pipeline leakage location.Thirdly, BP neural network has the shortage of slow convergence, low recognition rate and easy to converge to local minimum value. So the ant colony algorithm is introduced to optimize network's weights. To insure that the network has high training speed and recognition accuracy, the necessary number of hidden neurons is chosen by the excellent test method. Then the fusion structure of leakage characteristic parameters based on ant neural network is established for initial leak identification. Compared with BP neural network, the experiment result shows that the ant colony neural network model can not only improve the training speed, avoid network convergence to local optimal solution effectively, but also improve the accuracy rate of leakage recognition.Fourthly, considering that the diagnoisis results from different nodes may seriously conflict, it is difficult for the cluster head to make correct decisions using D-S or its modified combination rules. So a novel conflicting evidence combination algorithm based on reliability and coherence intensity (CECARCI) is proposed. The algorithm preprocesses the evidence set according to nodes' reliability. By introducing the coherence intensity of evidence and support degree of base element, it obtains reasonable combination sequences and manages the conflict. The numerical example shows that proposed method not only decreases the effect of unreliable evidences on the fusion result, but also can obtain more reasonable results with good convergence.Fifthly, a novel evidence decision rule based on set attribute and preference degree is proposed for reducing the risk of cluster head's decision-making. The method divides the decision problem into construction and evaluation level of belief interval. At the construction level, refined belief interval of base elements in power set is calculated based on the set's uncertainty measure and support degrees between two focal elements. At the evaluation level, preference degree is used to estimate and rank the defined belief interval. So the decision-making model is constructed. Comparing with other endpoint value decision rules, the numerical example shows that the proposed method can make full use of interval information and avoid the wrong decision-making.Sixthly, considering the D-S evidence theory can't deal with the fuzzy information in pipeline leakage monitoring network, novel fuzzy evidence reasoning method based on distance measure is proposed. In the method, as viewed from the point of distance between two fuzzy sets, the contribution of one focal element to the other elements' belief or plausibility function is defined, and the fuzzy evidence combination rule is established. Compared with other methods, experimental results show that proposed generalization method can catch more information from focal elements' change, and avoid the insensitivity problem of fuzzy belief function to focal elements' change.In summary, by researching on the key technologies of data pre-processing, heterogeneous characteristic parameters fusion and multi-nodes united decision-making, this paper presents the systemic solution to the multi-source data processing in pipeline leakage monitoring network. The hierarchical data fusion method based on wavelet neural network and D-S evidence theory can reduce the identification uncertainty of single sensor and node, and improve the recognition accuracy greatly.