CN102122351A - Intelligent identification method for pipeline defect on basis of RBF (Radical Basis Function) neural network - Google Patents
Intelligent identification method for pipeline defect on basis of RBF (Radical Basis Function) neural network Download PDFInfo
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Abstract
The invention provides an intelligent identification method for a pipeline defect on the basis of an RBF (Radical Basis Function) neural network, comprising the following steps of: (1) obtaining a pipeline defect flux leakage signal and a pipeline defect outline as detection data; (2) building an RBF neural network; (3) training and testing the neural network; and (4) predicting the pipeline defect outline by the tested neural network. The pipeline defect outline comprises the length, the width and the depth of the pipeline. With the intelligent identification method, finite tests are carried out, thus a pipeline defect outline prediction model is built. A computer simulation test is carried out for scientific prediction, and the pipeline defect outline can be accurately and quickly predicted.
Description
Technical field
The present invention relates to a kind of oil and gas pipes defective intelligent identification Method, the particularly a kind of oil and gas pipes defective intelligent identification Method that can discern defect of pipeline accurately and rapidly based on the RBF neural network.
Background technology
The information such as characteristic parameter, profile that reflects defective by the magnetic leakage signal that measures is the difficult point of Magnetic Flux Leakage Inspecting research.General settling mode is the mutual relationship of analyzing defect parameter and magnetic leakage signal, goes out the relevant information of defective according to the feature inverse of signal.The method that adopts experimental formula quantizes the physical dimension of defective, and this method is not to be available under the very high situation in the requirement to quantified precision, but quantizes for defective, and this method is difficult to meet the demands.Carry out the reconstruct of defective based on the reverse equation of magnetic dipole, this method has bigger advantage for the defective of identification simple shape, but the actual defects more complicated of oil and gas pipes, and form is varied, adopts this method calculated amount huge.Magnetic leakage signal is subjected to the combined effect of multiple factor, is difficult to find a simple corresponding relation between the physical dimension of magnetic leakage signal and defective, carries out the defective inverting according to certain characteristics and sets up the model of a defect recognition and also have very big difficulty.Artificial neural network provides practicable approach for solving this engineering problem, neural network is to imitate the biological treatment pattern to obtain the theory of Intelligent Information Processing function, it is conceived to the microcosmos network structure of brain, by a large amount of neuronic complicated connections, employing is by the method for the end to the top, by the formed parallel distributed mode of self study, self-organization and nonlinear kinetics, handle the pattern information that is difficult to languageization.
Summary of the invention
The object of the present invention is to provide a kind of a kind of defect of pipeline intelligent identification Method that can discern defect of pipeline accurately and rapidly based on the RBF neural network.
The object of the present invention is achieved like this:
Comprise the steps: that (1) obtains the defect of pipeline data; (2) set up the RBF neural network; (3) neural network is carried out training and testing; (4) utilization is predicted the defect of pipeline profile by the neural network of test.
Described defect of pipeline size comprises: the length of defect of pipeline, width, the degree of depth.
Described step (1) also comprise to the defect of pipeline outline data normalize to 0 and+normalization process between 1.
Described neural network comprises an input layer, a middle layer and an output layer.
Describedly neural network is carried out training and testing be, data are divided into two parts, preceding 70% is used for training network, is designated as training sample, and back 30% is used for supervising network, and one is designated as test samples; To the network repetition training, when error reaches 0.001 between predicted value and Monitoring Data, stop training, begin prediction.
The present invention only need carry out the limited number of time test, just can set up relevant defect of pipeline contour prediction model, and by computer simulation experiment, scientific forecasting can be predicted the defect of pipeline profile accurately and rapidly.
Description of drawings
Fig. 1 neural network structure figure;
The defect of pipeline prognostic chart and the actual figure comparison diagram of the training of Fig. 2 a-Fig. 2 f radial base neural net;
Fig. 3 defect of pipeline profile sample set table.
Embodiment
Below in conjunction with embodiment the defect of pipeline intelligent identification Method based on the RBF neural network of the present invention is made a detailed description.Defect of pipeline intelligent identification Method based on the RBF neural network of the present invention comprises the steps:
(1) obtains pipeline defect and magnetic leakage and defect of pipeline outline data.
The defect of pipeline size of obtaining in the embodiments of the invention is length, width, the degree of depth of pipeline.The process of the magnetic leakage signal prediction defect geometry parameter that produces according to defective comes down to a process of setting up the mapping relations of magnetic leakage signal and defect geometry parameter.All samples are divided into training sample set and test sample book collection.The length of sample, width, the degree of depth are shown in table 6.1.
The pipeline defect and magnetic leakage numerical value that obtains normalize to 0 and+1 between.
(2) set up the RBF neural network.
Described neural network is by input layer, a hidden layer (radially basic unit) and the feedforward neural network that linear output layer is formed.The principal character of RBF neural network is that hidden layer adopts radial basis function as neuronic activation function, makes it have the part and experiences characteristic.All data normalizations to 0 and+1 between.
(3) neural network is carried out training and testing.
Describedly neural network is carried out training and testing be, pipeline defect and magnetic leakage data and defect of pipeline profile are designated as one group of data, and all group data are divided into two parts, preceding 70% is called training sample, and back 30% is called test samples.
Training sample with 70% is used for training network, set up study mechanism, promptly when one group of data of input, promptly provide the such one group of input of defect of pipeline profile during data, through the automatic computing of network, have an output valve (the defect of pipeline profile of prediction), relatively the error between output valve and the desired output (the defect of pipeline profile of actual measurement), if error is less than designated precision, then study finishes.Otherwise, enter next group study, up to connect weights to the predicated error of all training groups all in specified scope, the best weight value of output this moment.The training group is many more, and the study of network is abundant more, and the network empirical value is big more, and precision of prediction is high more.To the network repetition training, when error reaches 0.001, stop training, begin prediction.Forecast model desired value and output valve related coefficient are up to 0.9887 at this moment, and root-mean-square error is 0.25500.
Test samples with other 30% is used for supervising network.After network training finishes, utilize other 30% data to come supervising network, see model gets whether to meet the requirements.Utilize the other 30% group of pairing defect of pipeline profile of neural network prediction, error between contrast model predication value and actual measured value, when the predicated error of each group test data all is lower than prescribed level, pass through test when neural network, can be used for prediction work.Related coefficient is 0.8376 between model predication value and measured value at this moment, and root-mean-square error is 0.5785, by test.In training, when all being lower than prescribed level, the predicated error of each group test data passes through test when neural network, can be used for prediction work.
(4) utilization is predicted by the neural network of test.
Utilization by the neural network of test predict must with input data normalization to 0 and+1 between, be input to again in the neural network by test, and the output after the network operations carried out anti-normalization, just obtain the defect of pipeline profile.
Above presentation of results, the neural network of being set up all has good prediction effect to training group and test group, thereby has stronger popularization ability.Present embodiment shows that the present invention can predict the defect of pipeline profile accurately and rapidly, and Forecasting Methodology has stronger popularization ability, has broad application prospects.
Claims (8)
1. defect of pipeline intelligent identification Method based on the RBF neural network is characterized in that:
(1) obtains pipeline defect and magnetic leakage and defect of pipeline outline data as detecting data;
(2) set up the RBF neural network;
(3) neural network is carried out training and testing;
(4) utilization is predicted by the neural network of test.
2. the defect of pipeline intelligent identification Method based on the RBF neural network according to claim 1 is characterized in that: length, width, the degree of depth that described defect of pipeline size is a pipeline.
3. the defect of pipeline intelligent identification Method based on the RBF neural network according to claim 2 is characterized in that: obtain pipeline defect and magnetic leakage and defect of pipeline outline data as detect after the data to pipeline defect and magnetic leakage and defect of pipeline outline data normalize to 0 and+1 between.
4. the defect of pipeline intelligent identification Method based on the RBF neural network according to claim 3 is characterized in that: described neural network comprises an input layer, a middle layer and an output layer.
5. the defect of pipeline intelligent identification Method based on the RBF neural network according to claim 4, it is characterized in that: describedly neural network is carried out training and testing be, to detect data and be divided into two parts, preceding 70% is used for training network, be designated as training sample, back 30% is used for supervising network, is designated as test samples; To the network repetition training, when error reaches 0.001 between predicted value and Monitoring Data, stop training, begin prediction.
6. the defect of pipeline intelligent identification Method based on the RBF neural network according to claim 5 is characterized in that: described training to neural network is to adopt radial basis function algorithm to train.
7. the defect of pipeline intelligent identification Method based on the RBF neural network according to claim 6 is characterized in that: when neural network to the predicated error of each group test samples when all being lower than prescribed level by test.
8. the defect of pipeline intelligent identification Method based on the RBF neural network according to claim 7, it is characterized in that: when utilization is predicted by the neural network of test, to detect earlier data normalization to 0 and+1 between, import again, and the output valve after the network operations carried out anti-normalization, promptly obtain defect of pipeline contour prediction value.
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| CN102364501A (en) * | 2011-09-14 | 2012-02-29 | 哈尔滨工程大学 | Method for reproducing two-dimensional defect of petroleum pipeline PSO-BP (Particle Swarm Optimization-Back-Propagation) neural network |
| CN102735747A (en) * | 2012-04-10 | 2012-10-17 | 南京航空航天大学 | Defect quantitative identification method of high-speed magnetic flux leakage inspection of high-speed railway rails |
| CN103440497A (en) * | 2013-08-13 | 2013-12-11 | 上海交通大学 | GIS insulation defect partial discharge atlas pattern recognition method |
| CN105321173A (en) * | 2015-09-23 | 2016-02-10 | 电子科技大学 | Machine vision based automatic defect detection method for train tunnel cable clamp |
| CN105334269A (en) * | 2015-10-19 | 2016-02-17 | 江苏大学 | Pipeline defect type determination method based on neural network and guided wave characteristic database |
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| CN108615234A (en) * | 2018-04-19 | 2018-10-02 | 清华大学 | Defect profile inversion method based on magnetic leakage signal |
| CN109613109A (en) * | 2018-12-19 | 2019-04-12 | 智云安科技(北京)有限公司 | A kind of Pipeline Magnetic Flux Leakage Inspection automatic data analysis system |
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