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.

An identification device according to one embodiment includes a vibration generation unit that generates, by a vibration generator, first vibrations to be provided to a three-dimensional object to be identified having an integrated structure; an acquisition unit that acquires, from a vibration detector, a detection signal corresponding to a second vibration that has propagated inside the three-dimensional object among the first vibrations provided to the three-dimensional object; a feature quantity generation unit that generates a feature quantity indicating a frequency characteristic of the second vibration, based on the acquired detection signal; and an identification unit that identifies the three-dimensional object to be identified, based on a feature quantity stored in a storage device in which the feature quantity indicating a frequency characteristic based on a vibration that has propagated inside a previously identified three-dimensional object is stored and on the feature quantity generated by the feature quantity generation unit.

A motor noise detecting device according to an embodiment of the present disclosure includes a signal sensing part for sensing an acoustic signal generated from an object to be tested, a data acquisition part for receiving the acoustic signal sensed by the signal sensing part and converting it into an acoustic digital signal, and a data analysis part for receiving and analyzing the acoustic digital signal to perform a detection on whether the object to be tested is abnormal. In addition, the signal sensing part includes an AE (Acoustic Emission) sensor for sensing an elastic wave included in the acoustic signal, and the data analysis part generates result data of analyzing the acoustic digital signal, analyzes the generated result data through a pre-learned model, and detects whether the object to be tested is abnormal.

Creation and use of a digital twin instance (DTI) for a physical instance of the part. The DTI may be created by a model inversion process such that model parameters are iterated until a convergence criterion related to a physical resonance inspection result and a digital resonance inspection result is satisfied. The DTI may then be used in relation to part evaluation including through simulated use of the part. The physical instance of the part may be evaluated by way of the DTI or the DTI may be used to generate maintenance schedules specific to the physical instance of the part.

Provided is a novel analysis method that enables identification of a sample even when any sample is introduced during measurement carried out by using a chemical sensor. An input in which the amount of an unknown sample changes over time is provided to the chemical sensor, a response which is from the chemical sensor and which changes over time is measured, a sensor function (transmission function) of the chemical sensor with respect to the unknown sample is calculated on the basis of the input and the response, and the unknown sample is identified on the basis of the sensor function of the chemical sensor with respect to the unknown sample.

A system includes one or more tools, sensors, or both configured to obtain data related to the one or more pipelines, wherein the data is ultrasonic data, electromagnetic data, or both, and a cloud-based computing system including at least one processor that receives the data from the one or more tools, sensors, or both, performs analysis to generate a virtual structural model of the one or more pipelines based on the data, determines one or more states of the one or more pipelines using the virtual structural model and determines whether to take one or more actions when the one or more states indicate that the one or more pipelines violate a threshold operation boundary.

An ultrasonic method and system for simultaneously measuring lubrication film thickness and liner wear of sliding bearings. The method includes: installing an ultrasonic sensor on a bearing bush; sending, by a processor, signals to an ultrasonic pulser-receiver to generate voltage pulses to excite the ultrasonic sensor to generate ultrasonic pulses; collecting an echo signal of an unworn liner-air interface as a reference signal B.sub.a(f); collecting an echo signal of worn liner-lubrication film interface as to-be-measured signal B.sub.ow(f); obtaining an amplitude spectrum |B.sub.a(f)| and a phase spectrum Φ.sub.B.sub.aof B.sub.a(f), an amplitude spectrum |B.sub.ow(f)| and a phase spectrum Φ.sub.B.sub.ow(f) of B.sub.ow(f) by FFT; calculating an amplitude spectrum |R.sub.w(f)|, and a phase spectrum Φ.sub.R.sub.w(f) of a reflection coefficient; based on |R.sub.w(f)|, calculating lubrication film thickness d via a resonance model or a spring model; and based on Φ.sub.R.sub.w(f), calculating liner worn thickness via wear model under different film thicknesses.

A stamping quality inspection system includes a stamping device, a signal detecting element, and a processor. The signal detecting element is coupled to the stamping device. The signal detecting element is configured to detect a sound signal and a vibration signal of the stamping device. The processor is coupled to the signal detecting element. The processor is configured to determine a stamping operation time interval according to the sound signal and the vibration signal, to compare a sub sound signal of the sound signal and a sub vibration signal of the vibration signal in the stamping operation time interval to a pattern comparison module, so as to generate a quality inspection result.

The present disclosure relates to the technical field of rail turnouts, and to a method for determining propagation characteristics of guided waves of a variable cross-section rail of a turnout. The method includes the following steps: step 1: establishing dispersion curves: separately calculating dispersion curves of sections of a variable cross-section rail, and fitting dispersion curves of different sections in a similar wave mode according to a longitudinal position to generate a “wavenumber-frequency-position” three-dimensional dispersion surface; step 2: analyzing dispersion characteristics: based on the “wavenumber-frequency-position” three-dimensional dispersion surface, using a semi-analytical finite element method to calculate a wavenumber-frequency dispersion curve and a guided wave structure of the characteristic section; and step 3: performing finite element simulation verification: establishing a switch rail model for simulation, then using two-dimensional fast Fourier transform (2D-FFT) to identify a frequency wavenumber dispersion curve of collected data.