公开(公告)号：US20240068951A1

公开(公告)日：2024-02-29

申请号：US17765215

申请日：2021-08-09

Applicant: SHANDONG UNIVERSITY

Inventor： Zhengfang WANG ,  Jing WANG ,  Wenqiang KANG ,  Hanchi LIU ,  Yuzhuang WAN ,  Qingmei SUI

IPC: G01N21/88 ,  B25J11/00 ,  G01N15/08 ,  G01N29/04 ,  G01N29/265 ,  G01N29/44 ,  G01S13/89 ,  G01S17/89

CPC classification number: G01N21/8851 ,  B25J11/00 ,  G01N15/08 ,  G01N29/043 ,  G01N29/265 ,  G01N29/4454 ,  G01S13/89 ,  G01S17/89 ,  G01N2291/0232 ,  G01N2291/0289 ,  G01N2291/2698

Abstract: A wall-climbing robot system and method for rapid nondestructive inspection of hidden defects in culverts and sluices. The system includes: a robot vehicle body; navigation positioning system and moving system, both mounted on the robot vehicle body; automatic knock inspection system, mounted at the vehicle body front end; mobile ultrasonic rapid inspection system and corrosion inspection system, dual-power system, formed by a non-contact negative pressure adsorption apparatus and rotor booster apparatuses, the non-contact negative pressure adsorption apparatus mounted on the vehicle body bottom, the rotor booster apparatuses mounted on two sides of the vehicle body; and a master controller, communicating with the navigation positioning system, moving system, automatic knock inspection system, mobile ultrasonic rapid inspection system, corrosion inspection system, and dual-power system. The system recognizes positions and categories of internal defects from ultrasonic data and perform concrete permittivity inversion and disease recognition on arbitrary length-continuous survey line ground-penetrating radar data.

公开(公告)号：US20210396842A1

公开(公告)日：2021-12-23

申请号：US17289139

申请日：2020-09-30

Applicant: SHANDONG UNIVERSITY

Inventor： Shucai LI ,  Bin LIU ,  Zhengfang WANG ,  Peng JIANG ,  Fengkai ZHANG ,  Hanchi LIU

IPC: G01S7/41 ,  G01S13/88 ,  G01N29/06 ,  G01M3/38 ,  G05D1/02 ,  G01N23/203 ,  B25J5/00 ,  B25J15/00 ,  G06N3/08

Abstract: A multi-scale inspection and intelligent diagnosis system and method for tunnel structural defects includes: a traveling section; a supporting section, disposed on the traveling section, and including a rotatable telescopic platform, where two mechanical arms working in parallel are disposed on the rotatable telescopic platform; an inspection section, mounted on the supporting section, and configured to perform multi-scale inspection on surface defects and internal defects in different depth ranges of a same position of a tunnel structure, and transmit inspected defect information to a control section; and the control section, configured to: construct a deep neural network-based defect diagnosis model; construct a data set by using historical surface defect and internal defect information, and train the deep neural network-based defect diagnosis model; and receive multi-scale inspection information in real time, and automatically recognize types, positions, contours, and dielectric attributes of the internal and surface defects.

公开(公告)号：US20210389257A1

公开(公告)日：2021-12-16

申请号：US17289280

申请日：2020-09-30

Applicant: SHANDONG UNIVERSITY

Inventor： Bin LIU ,  Zhengfang WANG ,  Peng JIANG ,  Wenqiang KANG ,  Hanchi LIU ,  Jiaqi ZHANG ,  Qingmei SUI

IPC: G01N21/954 ,  G01N22/02 ,  G01N23/203 ,  G01N23/20008 ,  F16L55/40 ,  G01N21/88 ,  G06N20/00

Abstract: A multi-arm robot used for tunnel lining inspection and defect diagnosis in an operation period, including a moving platform, where an environment detection device and a defect infection device are disposed on the moving platform, the defect infection device is disposed on the moving platform by using a multi-degree-of-freedom mechanical arm, and an attitude detection module is disposed on each multi-degree-of-freedom mechanical arm; a controller receives environmental data and mechanical arm attitude data sensed by the environment detection device and the attitude detection module, and sends a control instruction to the moving platform and the multi-degree-of-freedom mechanical arm according to the environmental data, to implement movement of the robot; and the controller receives tunnel lining structural data sensed by the defect infection device, and performs defect diagnosis. Overall automatic inspection can be implemented both on the surface and inside of the tunnel lining.