A sensing system contains a vibration device attached to a stem of an agricultural crop for applying vibration to the agricultural crop, at least one sensor attached to the stem of the agricultural crop for sensing vibration of the agricultural crop caused by the vibration applied to the agricultural product from the vibration device to transmit vibration information relate to the vibration of the agricultural crop and a computing device for identifying one local maximum value among a plurality of local maximum values in a frequency spectrum obtained from the vibration information received from the at least one sensor as a resonance frequency of the vibration of the agricultural crop to determine a growth degree of a fruit of the agricultural crop based on the identified resonance frequency.

Methods, systems, and computer storage media for providing an indication of an integrity of an object based on a non-invasive assessment of the integrity of the object using acoustic signature management engine in object integrity sensing system. In operation, an aggregate object-intermediate-medium sound of an object in an intermediate medium is detected (e.g., via sensors). An acoustic signature of the aggregate object-intermediate-medium sound is generated as a processed acoustic channel associated with statistical measurements. A reference acoustic signature of the object and intermediate medium is accessed. The reference acoustic signature is associated with an acoustic signature computation model, that generates reference acoustic signatures based on a mean and standard deviation measurements of input signals transmitted through the object and intermediate medium. A determination whether the object has impaired integrity is determined based on a quantified difference between the acoustic signature of the aggregate object-intermediate-medium sound and the reference acoustic signature.

Method and apparatus estimate the length of a fatigue crack in sheet metal structures from individual acoustic emission (AE) signals without recourse to the AE signal history or AE signal amplitude. AE energy generated at one crack tip travels to the other tip and establishes a standing wave pattern that has a characteristic dominant frequency which depends on the crack length. Therefore, crack length information can be recovered from the analysis of the standing wave frequency present in the high-frequency AE signals. We found that the AE signals predicted through numerical simulation have embedded in the high-frequency information that can be related directly to crack size. This information is manifested as peaks in the frequency spectrum that shift as crack length changes. The predictive AE models were tuned against experimentally observed AE signals and a methodology for predicting crack length from AE signals was established. This methodology was utilized to develop machine learning algorithms for predicting crack length directly from individual AE signals. Specific artificial intelligence methodology presently disclosed can estimate in real-time the crack length information from the high-frequency AE waveforms during fatigue crack growth.

Method and apparatus estimate the length of a fatigue crack in sheet metal structures from individual acoustic emission (AE) signals without recourse to the AE signal history or AE signal amplitude. AE energy generated at one crack tip travels to the other tip and establishes a standing wave pattern that has a characteristic dominant frequency which depends on the crack length. Therefore, crack length information can be recovered from the analysis of the standing wave frequency present in the high-frequency AE signals. We found that the AE signals predicted through numerical simulation have embedded in the high-frequency information that can be related directly to crack size. This information is manifested as peaks in the frequency spectrum that shift as crack length changes. The predictive AE models were tuned against experimentally observed AE signals and a methodology for predicting crack length from AE signals was established. This methodology was utilized to develop machine learning algorithms for predicting crack length directly from individual AE signals. Specific artificial intelligence methodology presently disclosed can estimate in real-time the crack length information from the high-frequency AE waveforms during fatigue crack growth.

Use of a sensor read out system with at least one fiber optical sensor, which is connected via at least one signal line to a processing unit as part of an additive manufacturing setup, for in situ and real time quality control of a running additive manufacturing process. Acoustic emission is measured via the at least one fiber optical sensor in form of fibers with Bragg grating, fibre interferometer or Fabry-Perot structure, followed by a signal transfer and an analysis of the measured signals in the processing unit, estimation of the sintering or melting process quality due to correlation between sintering or melting quality and measured acoustic emission signals and subsequent adaption of ion and electron beams, microwave or laser sintering or melting parameters of a ion and electron beams, microwave or laser electronics of the additive manufacturing setup in real times via a feedback loop as a result of the measured acoustic emission signals after interpretation with an algorithmic framework in the processing unit.

Use of a sensor read out system with at least one fiber optical sensor, which is connected via at least one signal line to a processing unit as part of an additive manufacturing setup, for in situ and real time quality control of a running additive manufacturing process. Acoustic emission is measured via the at least one fiber optical sensor in form of fibers with Bragg grating, fibre interferometer or Fabry-Perot structure, followed by a signal transfer and an analysis of the measured signals in the processing unit, estimation of the sintering or melting process quality due to correlation between sintering or melting quality and measured acoustic emission signals and subsequent adaption of ion and electron beams, microwave or laser sintering or melting parameters of a ion and electron beams, microwave or laser electronics of the additive manufacturing setup in real times via a feedback loop as a result of the measured acoustic emission signals after interpretation with an algorithmic framework in the processing unit.

A system for estimating both thickness and wear state of refractory material (1) of a metallurgical furnace (12), including at least on processor including a database of simulated frequency domain data named simulated spectra representing simulated shock waves reflected in simulated refractory materials of known state and thickness, each simulated spectrum being correlated with both known state and thickness data of the considered simulated refractory material, wherein the at least one processor is configured to record a reflected shock wave as a time domain signal, and to convert it into frequency domain data named experimental spectrum, and are further configured to compare the experimental spectrum with at least a plurality of simulated spectra from the database, to determine the best fitting simulated spectrum with the experimental spectrum and to estimate thickness and state of the refractory material (1) of the furnace (12) using known state and thickness data correlated with the best fitting simulated spectrum.

A system for estimating both thickness and wear state of refractory material (1) of a metallurgical furnace (12), including at least on processor including a database of simulated frequency domain data named simulated spectra representing simulated shock waves reflected in simulated refractory materials of known state and thickness, each simulated spectrum being correlated with both known state and thickness data of the considered simulated refractory material, wherein the at least one processor is configured to record a reflected shock wave as a time domain signal, and to convert it into frequency domain data named experimental spectrum, and are further configured to compare the experimental spectrum with at least a plurality of simulated spectra from the database, to determine the best fitting simulated spectrum with the experimental spectrum and to estimate thickness and state of the refractory material (1) of the furnace (12) using known state and thickness data correlated with the best fitting simulated spectrum.

A resonance detection system includes a vibration simulation mechanism and a vibration audio analysis device. The vibration simulation mechanism includes a mechanism body that accommodates a peripheral interface device. The vibration simulation mechanism generates a vibration wave to the peripheral interface device. The peripheral interface device generates a vibration audio signal in response to the vibration wave. The vibration audio analysis device is electrically connected with the vibration simulation mechanism. After the vibration audio signal is inputted into the vibration audio analysis device, the vibration audio analysis device judges whether there is an abnormal resonance phenomenon in the vibration audio signal. The vibration simulation mechanism further includes a patch-type audio collector, which is electrically connected with the vibration audio analysis device. The patch-type audio collector is attached on the mechanism body containing the peripheral interface device. The vibration audio signal is collected by the patch-type audio collector.

A method for analyzing dynamic mode change of an anisotropic material includes performing modal analysis of a first physical force applied to an isotropic material specimen and a first vibration signal collected from the isotropic material specimen, acquiring a first modal parameter of the isotropic material specimen, based on the modal analysis result, performing modal analysis of a second physical force applied to the anisotropic material specimen and a second vibration signal collected from the anisotropic material specimen, acquiring a second modal parameter of the anisotropic material specimen, based on the modal analysis result, acquiring a modal assurance criterion (MAC) for each mode of the anisotropic material specimen, based on the first and second modal parameters, and acquiring each similar mode of the anisotropic material specimen to each mode of the isotropic material specimen.