Northeastern University

Jian Feng

Huaguang Zhang

Gang Wang

Jinhai Liu

Jun Yang

Qiuye Sun

Dazhong Ma

Dongsheng Yang

Zhiliang Wang

Yanhong Luo

Guotao Hui

Da Zhong Ma

Zhenwei Liu

Xinrui Liu

Nan Wang

Bonan Huang

Lei Wang

Jiayue Sun

Senxiang Lu

XuanZhe Peng

Xue Liang

Jilei Li

Feiye Wu

Xiangxiang Wei

Zhen Wei Liu

Xin Gang Zhang

Jin Hai Liu

Ming Rui Fu

Sen Xiang Lu

Xiaoyu Li

Hua Guang Zhang

Ge Yu

Hong Qiu Wei

Zhenning Wu

Xin Bo Zhang

Provided is an intelligent inversion method for pipeline defects based on heterogeneous field signals. The method includes the following steps: firstly, acquiring heterogeneous field signals, performing an abnormality judgement, then correcting base values of the heterogeneous field signals, and performing denoising treatment; padding the denoised heterogeneous field signals corresponding to the pipeline defects, unifying the heterogeneous field signals of different sizes into the heterogeneous field signals of same sizes, and performing a nonlinear transformation on signal amplitudes; designing a sparse autoencoder with an axisymmetric structure, and obtaining primary characteristics of the heterogeneous field signals; classifying the pipeline defects according to lengths, widths and depths to obtain category labels of the pipeline defects; designing a multi-classification neural network to classify the heterogeneous field signals, and extracting deep characteristics containing defect size information; and constructing a random forest regression model to realize intelligent inversion for sizes of the pipeline defects.

Intelligent inversion method for pipeline defects based on heterogeneous field signals

Provided is an intelligent analysis system for inner detecting magnetic flux leakage (MFL) data in pipelines, including a complete data set building module, a discovery module, a quantization module and a solution module, wherein: a complete data set building method is adopted in the complete data set building module to obtain a complete magnetic flux leakage data set; a pipeline connecting component discovery method is adopted in the discovery module to obtain the precise position of a weld; an anomaly candidate region search and identification method is adopted in the discovery model to find out magnetic flux leakage signals with defects; a defect quantization method based on a random forest is adopted in the quantization module to obtain a defect size; and a pipeline solution based on an improved ASME B31G standard is adopted in the solution module to output an evaluation result.

Intelligent analysis system using magnetic flux leakage data in pipeline inner inspection

The invention provides a reactive power optimization system and a method of a power grid based on a double-fish-swarm algorithm. The system includes a power grid state data acquiring module, a reactive power regulating module and a reactive power executing module. The power grid state data acquiring module includes a power grid state data acquiring processor and a relay transmitter. The reactive power regulating module is a control terminal. The reactive power executing module includes generator terminal voltage regulators, transformer tap regulators and reactive power compensation regulators. The method is used for acquiring the initial data to be optimized in the current network; and optimizing the initial data to be optimized in the current network based on a double-fish-swarm algorithm so as to obtain optimal value of control variables in the power grid. According to the method, the distribution network to be optimized can realize reasonable reactive power flow distribution.

Reactive power optimization system and method of power grid based on the double-fish-swarm algorithm

The present invention relates to an intelligent adaptive system and method for monitoring leakage of oil pipeline networks based on big data. The present invention effectively analyzes a large amount of data collected on site within a reasonable time period and obtains a state of a pipeline network by an intelligent adaptive method, thereby obtaining a topological structure of a pipeline network. The present invention specifically adopts a flow balance method in combination with information conformance theory to analyze whether the pipeline network has leakage; small amount of leakage and slow leakage can be perfectly and accurately alarmed upon detection; as a generalized regression neural network is adopted to locate a leakage of the pipeline network, an accuracy of a result is increased. Therefore, the present invention adopts a policy and intelligent adaptive method based on big data to solve problems of detecting and locating leakage of the pipeline network.

Intelligent adaptive system and method for monitoring leakage of oil pipeline networks based on big data

Provided are a bypass type 220V grid voltage sag prevention device and a control method therefor. The bypass type 220V grid voltage sag prevention device comprises an AC-to-DC energy storage unit, a Single Chip Microcomputer (SCM) control and display unit, and an inverter output unit. The AC-to-DC energy storage unit comprises a rectifier circuit, an energy storage control circuit and a quick tank circuit; the SCM control and display unit comprises an SCM, a DC step-down module circuit and an energy storage voltage detecting circuit; and the inverter output unit comprises a high-frequency PWM generating circuit, a high-frequency transformer output circuit, a transformer output full-bridge rectifier circuit, a PWM generating circuit, a PWM driving and full-bridge inverter circuit, a 220V grid zero-cross point detecting circuit, a 220V grid voltage detecting circuit, a 220V grid and inversion AC converting circuit, and a signal interface circuit.

Bypass type 220V grid voltage sag prevention device and control method therefor

The present invention relates to a device for positioning submarine pipeline robots and method. The device comprises a pressure wave generator and a device for pressure wave acquiring and processing. The method comprises the following steps of (1) determining the generation mode; (2) acquiring pressure signals; (3) filtering pressure signals; (4) dividing the filtered pressure signals of into the same group; (5) identifying the data during this period in real time; (6) determining the moving state of the robot in the pipeline; (7) calculating the position of the robot in the pipeline; (8) ending the operations, otherwise repeating Step 4 to Step 7 continuously. The present invention has the advantages that the position of submarine pipeline robots can be located in real time. In addition, the severe environment around the pipelines can hardly affect the performance under this method.

Device for positioning submarine pipeline robots and method thereof

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Shenyang, China

Jian Feng