The present invention discloses a boom monitoring method and engineering machinery comprising a boom monitoring system. The method comprises obtaining a boom damage signal monitored in boom operation by a piezoelectric sensing network formed by a plurality of piezoelectric sensors arranged at different points on a boom, and determining a damage position of the boom and a corresponding first boom damage value such that when the first boom damage value reaches a preset starting value of an optical fiber sensing network formed by a plurality of optical fiber sensors arranged at the different monitoring points on the boom, optical wave values of the corresponding monitoring points are obtained and a boom crack signal is determined. A second boom damage value is calculated according to the boom crack signal, which comprises a crack change factor and a crack length. According to the present invention, the boom is monitored with improved efficiency.

A scanning device is provided. The scanning device includes a frame having a first portion and a second portion pivotably coupled to the first frame portion. The scanning device also includes a couplant source disposed in the first frame portion along with a couplant assembly. The couplant assembly includes a first couplant line disposed completely within the first frame portion and the second frame portion. The couplant assembly also includes a second couplant line extending from the first couplant line and out of the second frame portion at a first end of the second couplant line. The couplant assembly has a couplant line branch extending from the second couplant line where a sensor assembly of the ultrasound scanning device couples with the couplant line branch at an end opposite the second end of the second couplant line.

Provided is a hammering test system. A hammering test system includes a hammering test device including a target, a traveling mechanism for automatically traveling on a to-be-tested surface, a marking mechanism configured to perform marking on the to-be-tested surface, an adsorbing mechanism for adsorbing to the to-be-tested surface, and a hammering test mechanism configured to conduct a hammering test on the to-be-tested surface, and a surveying instrument capable of performing automatic tracking and distance and angle measurements of the target. A hammering test is conducted by causing the hammering test device to travel to a desired position while adsorbing to a to-be-tested surface by the adsorbing mechanism. When it is determined that there is an abnormality, a marking is marked on the to-be-tested surface. The surveying instrument automatically tracks the target, and when conducting a hammering test, makes distance and angle measurements of the target.

Embodiments provide systems and methods for utilizing acoustic sensors to detect defects via online or in situ monitoring of additive manufacturing (AM) processes. Sensors may capture acoustic waves associated with AM manufacturing operations. The acoustic emissions in combination with other sensing data, such as cameras or thermometers, may be used to characterize the state of the AM process, such as to detect a defect has occurred or confirm a defect has not occurred. When defects are detected, the AM process may be stopped to prevent further processing of a defective part. When defects are predicted as likely to occur, operational parameters of the AM device or process may be adjusted to mitigate the occurrence of a defect. The techniques disclosed herein enable detection of defects that occur underneath the surface of the part being manufactured, as well as correct issues with the AM device or process before a defect occurs.

Structural health monitoring (SHM)/nondestructive evaluation (NDE) exists as a tool in conjunction with manufactured pieces. Presently disclosed subject matter integrates automated video with a structural health monitoring system. In conjunction with bridge monitoring, integration of such two systems automates determination of the effect or correlation of vehicular loading on SHM data from a subject bridge. Such correlations help to understand the sources of structural health monitoring data, particularly acoustic emission data, in bridges and other structures, such as dams and nuclear plants. Automation of the evaluation of bridges and other structures increases accuracy and minimizes risk to workers and the public. Assessing the structural condition of bridges and other structures as presently disclosed also facilitates automated asset management of transportation systems, such as by state departments of transportation and other bridge/structural owners.

There is described a probe for non-destructive testing of a curved object, the probe comprising an arrangement of magnets and coils configured for generating shear horizontal guided waves for propagating longitudinally in the object, the probe having a top surface, a bottom surface, and two opposed ends extending between the top surface and the bottom surface, the bottom surface having a non-zero curvature between the two opposed ends and matable with an outer surface of the curved object.

Inspection robots and methods for inspection of curved surfaces with sensors at selected horizontal distances are described. An example of such an inspection robot includes a housing; a drive module with a wheel and a motor operatively linked to the housing, a plurality of sensor sleds, and a payload. The payload, which is coupled to the housing, may include a first and a second rail component, each with at least one connector, where the rail components are connectable at a first selected position of a plurality of discrete engagement positions. Each of the rail components may be structured to support at least one of the plurality of sleds where each of the plurality of sleds is coupled to the payload at a respective selected horizontal position such that the plurality of sleds are at selected horizontal distances from each other.

An inspection method according to the present disclosure includes a step of mounting an ultrasonic probe, a step of mounting a pulser receiver, a step of causing the ultrasonic probe to transmit ultrasonic waves, a step of causing the ultrasonic probe to receive a reflected wave of the ultrasonic waves reflected by the wind turbine blade, a step of causing the pulser receiver to acquire reflected-wave data, a step of causing the pulser receiver to wirelessly transmit the reflected-wave data, a step of causing at least one of antennas to receive the wirelessly transmitted reflected-wave data, and a step of causing an information processing device electrically connected to the at least two antennas to perform information processing on the reflected-wave data.

A moving inspection device inspecting an inspection target while realizing the simplification of the configuration and significant size reduction/weight reduction of the device, a moving inspection method, and a method for manufacturing a steel material. The device includes a moving inspection device body configured to inspect an inspection target for defects while moving over its surface. The moving inspection device body includes: a carriage that moves by at least two wheels that rotate forward and backward over the surface; and at least one inspection sensor on the front end side or the rear end side of the carriage. An inspection region of the inspection target is divided into two divided regions across a straight line, and the carriage is configured to move when the inspection sensor is directed to side edges sides of the divided regions facing the straight line in each of the two divided regions.

An acoustic inspection system can be used to generate a surface profile of a component under inspection, and then can be used to perform the inspection on the component. The acoustic inspection system can obtain acoustic imaging data, e.g., FMC data, of the component. Then, the acoustic inspection system can apply a previously trained machine learning model to an encoded acoustic image, such as a TFM image, to generate a representation of the profile of one or more surfaces of the component. In this manner, no additional equipment is needed, which is more convenient and efficient than implementations that utilize additional components that are external to the acoustic inspection system.