A method for inferring an inferred speed of sound of a flow fluid is disclosed. The method is conducted by a computer system (200) having a processor (210) and a memory (220), the processor (210) configured to execute instructions from the memory (220) and store data in the memory (220), the memory (220) having a SoS inference module (202). The method includes inferring, by the SoS inference module (202), the inferred speed of sound of the flow fluid based on an inferential relationship between a measured density of the flow fluid and the inferred speed of sound of the flow fluid.

According to one embodiment, a structure evaluation system of the embodiments includes a plurality of sensors, an arrival time determiner, a reliability calculator, and a map generator. The plurality of sensors detect elastic waves. The arrival time determiner determines arrival times of the elastic waves using elastic waves detected by the plurality of respective sensors. The reliability calculator calculates reliabilities related to measurement waveforms of the elastic waves on the basis of the arrival times. The map generator generates a first map on the basis of the calculated reliabilities or the reliabilities and a distance.

A damage evaluation device includes: a phased array probe that irradiates an ultrasonic signal from a surface of an inspection metal toward an inside of the inspection metal and detects a reflection signal reflected in a predetermined region inside the inspection metal; and an arithmetic processor. The arithmetic processor sets planes parallel to each other in an inspection region, calculates pixel data by quantifying the reflection signal from segments set in each of the planes; calculates a scattering degree of the pixel data, and evaluates damage of the inspection metal based on the scattering degree.

An embodiment of the present disclosure may provide a method of detecting a fault location using an acoustic emission signal, including a measuring step of measuring, by a signal measuring unit including at least three sensors disposed in a diagnosed subject and isolated from one another, an acoustic emission signal generated from a faulty part of the diagnosed subject, a signal pre-processing step of filtering and amplifying, by the signal pre-processing unit, the acoustic emission signal, an extraction step of extracting, by a data operation unit, a measuring time, that is, the time when the acoustic emission signal reaches each of the at least three sensors of the signal measuring unit, and a first analysis step of analyzing, by a data analysis unit, a location and occurrence time of the faulty part by using the measuring time and location information of the signal measuring unit.

The present disclosure relates to a technical virtual sensing idea of indirectly measuring structural vibration information on an unmeasured point while minimizing the number of sensors attached for actual measurement, and more particularly, to a technique of estimating measurement data of an unmeasured point using a finite element model, synchronized and updated based on experimental data of an actual measurement subject structure, and a virtual sensing algorithm.

A system and method to evaluate and/or classify non-destructive testing sensor data, the system and method including: a transmitter configured to provide energy to a material; one or more sensors configured to convert the energy returned from the material into sensor data; a receiver configured to receive sensor data; an attenuation inversion module configured to apply a mathematical transformation to the sensor data to provide transformed sensor data; an analysis module configured to process the transformed sensor data to provided processed sensor date, by: determining values from the transformed sensor data; applying mathematical transformations to the values to produce a set of single values that represent the sensor data; a classification module configured to classify the processed sensor data; and an output module configured to output the results of the classification.

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.

Embodiments herein generally relate to systems and methods to determine the composition, properties, and morphology of a liquid in a liquid handling structure. Aspects disclosed include exploiting spatiotemporal constraints of zero-group-velocity modes for non-contact, non-invasive, liquid sensing applications.

A method and system for identifying a cavity position of a structure based on global search includes: step 1: using a structure requiring cavity position identification as a target area, arranging acoustic emission sensors at key positions of the target area, and acquiring actual travel time of signals between the acoustic emission sensors on site; step 2: constructing cavity models for the target area; and for each cavity model, tracking shortest paths of signal propagation between the acoustic emission sensors when each cavity model exists in the target area, to obtain theoretical travel time of the signals; and step 3: respectively calculating deviations between the theoretical travel time and the actual travel time of the signals between the acoustic emission sensors corresponding to each cavity model, and using a position of a cavity model corresponding to a minimum deviation as an identified cavity position in the target area.

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.