Embodiments herein provide a system (100) to estimate the amplitude of oscillations in a turbulent flow system (102) that exhibits oscillatory instabilities. The system (100) comprises of a sensor (102A) mounted on the turbulent flow system (102) to detect an oscillatory variable in the system obtaining a signal, a signal conditioner (104) that conditions the signal from the sensor, an amplitude estimator (110) that estimates the amplitude of the limit cycle oscillations, and also predict the proximity of the system to the oscillatory instability, a processor (108) connected to the amplitude estimator (110) to compare the predicted oscillation amplitude with a threshold value. The amplitude is estimated by estimating the spectral measure of the time series signal obtained from the system.

A system and method of monitoring a tubular structure is provided. The method includes: a) sensing one or more parameters relating to the tubular structure at spaced apart positions along the length of the tubular structure using a sensor module array having a plurality of sensor modules disposed in a cable attached to the tubular structure, the plurality of sensor modules producing communication signals representative of the sensed parameter at each position along the length of the tubular structure; and b) using a control unit to communicate with the sensor modules in the array, including receiving communication signals representative of the sensed parameter at each position along the length of the tubular structure, and processing the communications signals to produce information relating to the sensed parameter at the positions along the length of the tubular structure.

Systems, methods, and devices for fluid conduit inspection using absolute velocity of a sensor device are provided. The method includes: receiving sensor data collected by a sensor device during a measurement run from an interior of the fluid conduit while traveling along a length of the fluid conduit, the sensor device including a first magnetometer and a second magnetometer each having a fixed position in the sensor device, the fixed positions defining a separation distance between the first magnetometer and second magnetometer, the sensor data including magnetic flux data comprising first magnetic flux data collected by the first magnetometer and second magnetic flux data collected by the second magnetometer; determining a time delay between when a magnetic signal is present in the first magnetic flux data and when the magnetic signal is present in the second magnetic flux data; determining an absolute velocity of the sensor device.

A hammering test system includes a hammering test device including a target, a flying unit, and a hammering test mechanism configured to conduct a hammering test on a test object, a surveying instrument including a scanner for acquiring point cloud data by scanning with scanning light, and configured to be capable of performing tracking and distance and angle measurements of the target, and an arithmetic processing unit including a point cloud data analyzing unit configured to calculate shape data by analyzing point cloud data acquired by the scanner, and a flight plan calculating unit configured to calculate a flight plan of the hammering test device based on the shape data calculated by the point cloud data analyzing unit, and the surveying instrument tracks the target of the hammering test device and makes distance and angle measurements when the hammering test mechanism conducts a hammering test.

The present disclosure discloses a method for calculating an internal explosion load speed based on an incremental crack growth distance of a pipeline. The method includes steps of: respectively measuring at least three groups of distances between neighboring markings on forward and backward crack surfaces, and calculating the average values respectively to obtain the average incremental growth distances of forward and backward cracks; calculating the natural vibration frequency of the pipeline; and setting the ratio of backward crack speed to forward crack speed of the pipeline, then calculating the internal explosion load speed of the pipeline by a formula. The present disclosure provides a new effective method for calculating the internal explosion load speed based on the available parameters of the ruptured pipeline after explosion, which can provide a comparatively accurate estimation of internal explosion load speed, thereby providing references for inferring the explosion type occurred in the pipeline.

A device (10) for monitoring strain of an elongate member (12) is deployed underwater. The device (10) comprises a first clamp (14) configured to embrace and couple to the elongate member (12) at a first axial location, a second clamp (16) configured to embrace and couple to the elongate member at a second axial location separated from the first axial location, and a sensor which is responsive to an angle between the first clamp and the second clamp.

A structural health monitoring system comprises a first set of sensors operable for coupling to a structure positioned under ground, the first set of sensors further configured to detect an impact upon the structure while the first set of sensors is positioned under the ground; a second set of sensors operable to be positioned on or proximate to a surface of the ground, the second set of sensors further configured to detect an audible event occurring at a distance from the second set of sensors and the structure; and a computer readable memory storing one or more audio signatures that may correspond to the audible event.

Methods of detecting pipeline weakening are described herein. The methods include creating a pressure wave in a fluid flowing in a pipeline using an input transducer located at a first position along the pipeline; measuring the pressure wave using an output transducer positioned at a second position along the pipeline that is spaced from the first position, and generating an output signal based on the pressure wave; analyzing the output signal to determine a stiffness of a sidewall of the pipeline positioned between the input transducer and output transducer; and determining if the sidewall includes a defect based on the stiffness of the sidewall, including analyzing a frequency response of the output signal to detect the defect.

A system includes several load cells, each coupled to a lashing that secures shipping containers, accelerometer cells, each coupled to a shipping container at the top of a stack, and an inclinometer cell coupled to a cargo ship. The cells are configured to transmit data to a computer. All the cells comprise a processor programmed to acquire a time series of measurements and, preferably, decompose the time series into a sum of sinusoidal signals, each having a frequency and an amplitude. Alternatively, the computer can be programmed to decompose the time series. The computer is programmed to identify the signal components caused by the roll of the cargo ship or by resonances in shipping container stacks. The computer estimates cumulated damages caused by fatigue in the lashings and/or twist locks. The system is used to trigger alarms and/or schedule maintenance.

A permittivity sensor, for determining an image of a distribution of permittivity of a material of an object in a scene, comprising an input interface, a hardware processor, and an output interface is provided. The input interface is configured to accept phaseless measurements of propagation of a known incident field through the scene and scattered by the material of the object in the scene. The hardware processor is configured to solve a multi-variable minimization problem over unknown phases of the phaseless measurements and unknown image of the permittivity of the material of the object by minimizing a difference of a nonlinear function of the known incident field and the unknown image with a product of known magnitudes of the phaseless measurements and the unknown phases. Further, the output interface is configured to render the permittivity of the material of the object provided by the solution of the multi-variable minimization problem.