The present disclosure proposes a non-baseline on-line stress monitoring system and monitoring method based on multi-mode Lamb wave data fusion. A Lamb wave dispersion curve is established according to geometric dimensions and material parameters of a measured object, a cut-off frequency of a first-order Lamb wave mode is obtained, an excitation frequency of a Lamb wave signal is determined, and then pure Lamb waves in S0 and A0 modes obtained inside the measured object are obtained; an acoustoelastic equation is established, an elastodynamic equation of the measured object under a prestress condition is solved, and linear relationships between a group velocity and a stress of the Lamb waves in the S0 and A0 modes under the excitation frequency are obtained; data is processed through the on-line monitoring system; a stress gradient in a depth direction is calculated, and finally, a stress state of the measured object is represented. The present disclosure does not require data under a zero stress state as baseline data, does not require designing a wedge block capable of generating a critical refraction longitudinal wave, and combines acoustoelastic effects of Lamb waves in different modes to realize online stress monitoring without the baseline data.

A method, system, device, and medium for online stress monitoring without baseline data based on single-mode multi-frequency signal fusion are provided. The method includes: establishing a dispersion curve according to geometric dimensions and material parameters of a measured object; then solving an approximate linear relationship between propagation time of S0 modes with different frequencies and stress at a fixed propagation distance by using a relationship between stress and group velocity, the obtained linear relationship being an acousto-elastic equation required for final measurement; then performing Hilbert transformation on an obtained signal, extracting a signal envelope, and determining arrival time of two excitation frequency signals by means of a peak extraction algorithm and a time domain width of an excitation signal; and calculating a propagation time ratio and substituting the propagation time ratio into a pre-calibrated acousto-elastic equation to solve a stress value of an object to be measured. The disclosure is advantageous in that the multi-frequency data is fused by using dispersion characteristics of a single-mode Lamb wave and an acousto-elastic effect, thereby achieving online stress monitoring without baseline data.

A method, system, device, and medium for online stress monitoring without baseline data based on single-mode multi-frequency signal fusion are provided. The method includes: establishing a dispersion curve according to geometric dimensions and material parameters of a measured object; then solving an approximate linear relationship between propagation time of S0 modes with different frequencies and stress at a fixed propagation distance by using a relationship between stress and group velocity, the obtained linear relationship being an acousto-elastic equation required for final measurement; then performing Hilbert transformation on an obtained signal, extracting a signal envelope, and determining arrival time of two excitation frequency signals by means of a peak extraction algorithm and a time domain width of an excitation signal; and calculating a propagation time ratio and substituting the propagation time ratio into a pre-calibrated acousto-elastic equation to solve a stress value of an object to be measured. The disclosure is advantageous in that the multi-frequency data is fused by using dispersion characteristics of a single-mode Lamb wave and an acousto-elastic effect, thereby achieving online stress monitoring without baseline data.

The invention comprises a device and method to estimate the elasticity of soft elastic solids from surface wave measurements. The method is non-destructive, reliable and repeatable. The final device is low-cost and portable. It is based in audio-frequency shear wave propagation in elastic soft solids. Within this frequency range, shear wavelength is centimeter sized. Thus, the experimental data is usually collected in the near-field of the source. Therefore, an inversion algorithm taking into account near-field effects was developed for use with the device. Example applications are shown in beef samples, tissue mimicking materials and in vivo skeletal muscle of healthy volunteers.

Systems and techniques for measuring process characteristics including electrolyte distribution in a battery cell. A non-destructive method for analyzing a battery cell includes determining acoustic features at two or more locations of the battery cell, the acoustic features based on one or more of acoustic signals travelling through at least one or more portions of the battery cell during one or more points in time or responses to the acoustic signals obtained during one or more points in time, wherein the one or more points in time correspond to one or more stages of electrolyte distribution in the battery cell. One or more characteristics of the battery cell are determined based on the acoustic features at the two or more locations of the battery cell.

A method for automatic diagnosis of a mechanical system of a group of mechanical systems sharing mechanical characteristics includes obtaining data relating to a vibration. The vibration-related data is acquired by a portable communications device configured to communicate with a remote processor. The processor automatically diagnoses the mechanical system by applying a relationship to the obtained vibration-related data. The relationship is based on sets of vibration-related data previously obtained from the mechanical systems. Each set of vibration-related data relates to vibrations of a mechanical system. The relationship is further based on sets of operation data previously obtained for mechanical systems of the group. Each set of operation data indicates a previous state of operation of a mechanical system. Each of the previous states of operation is associated with at least one of the previously obtained sets of vibration-related data.

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

Systems and methods for prediction of state of charge (SOH), state of health (SOC) and other characteristics of batteries using acoustic signals, includes determining acoustic data at two or more states of charge and determining a reduced acoustic data set representative of the acoustic data at the two or more states of charge. The reduced acoustic data set includes time of flight (TOF) shift, total signal amplitude, or other data points related to the states of charge. Machine learning models use at least the reduced acoustic dataset in conjunction with non-acoustic data such as voltage and temperature for predicting the characteristics of any other independent battery.

The apparatus for inspecting the electrostatic chuck for substrate processing includes the electrostatic chuck including a ceramic layer and an electrode layer coupled to an inside of the ceramic layer, an ultrasonic sensor unit disposed on the electrostatic chuck, allowing an ultrasonic wave to be incident into the electrostatic chuck, and converting a reflected signal reflected through the electrostatic chuck into an ultrasonic voltage signal, and an ultrasonic inspection unit to divide the ceramic layer and the electrode layer, based on a size value of the ultrasonic voltage signal.

A method for inspecting a fastened structure, the fastened structure having at least one structural member defining a bore therein and a mechanical fastener received in the bore, includes applying acoustic energy to the fastened structure, the acoustic energy being applied over a plurality of frequencies, measuring a response of the fastened structure across at least two frequencies of the plurality of frequencies, and comparing the response of the fastened structure at the at least two frequencies of the plurality of frequencies to predefined values for the at least two frequencies of the plurality of frequencies to determine whether an out-of-tolerance condition is present.