An apparatus and a computer implemented method for determining material quality of a component, the method comprising: receiving ultrasonic scan data for a plurality of scanned components; maintaining the scan data within a data storage system; determining historical data associated with multiple parameters based on the scan data of the data storage system; generating a testing model using the historical data, wherein the testing model is configured to define multiple quality ranges for each parameter; scanning a component using at least one ultrasonic probe to provide component data; and determining quality information of the component using the testing model and the component data.

A method of determining wear/degradation levels of a consumable assembly of a welding/plasma torch may utilize a controlled sound signal in order to determine an acoustic profile or full spectral audio analysis dataset of the consumable assembly that facilitate the identification of patterns that correlate to certain wear/degradation levels of the consumable assembly. The full spectral audio analysis dataset may be obtained by subjecting a given consumable assembly to a controlled sound signal between operations and as the consumable assembly degrades over time. The full spectral audio analysis may serve as a wear/degradation profile over the life of the given consumable assembly. With a full dataset known for a particular consumable assembly model, an acoustic profile of another consumable assembly of the same model may be obtained and compared to the full dataset in order to identify the wear/degradation level of the tested consumable assembly.

This disclosure relates generally to detection of concentration of micro and nano particles in a fluid environment. An acoustic transmitter array is selective coated with polymer and receiver array is deployed at a random location in a conduit. The acoustic transmitter array on the conduit is insonified at a predetermined frequency to obtain a plurality of reflected signals. A plurality of key features pertinent to the conduit are extracted from the plurality of reflected signals to obtain a plurality of acoustic signals. A correlation model is configured by inputting, at least one feature associated with the pre-processed acoustic signals. A known concentrations of nano and micro particles are trained with an artificial neural network algorithm and calibrated with ground truth data. The location of the transmitter array and receiver array and the correlation model are finalized for detecting concentration of the particular micro and nano particles in the fluid environment.

In some implementations, a system may receive a cable map for a deployed cable. The system may receive vibration data indicating a vibration associated with a first section of the cable. The system may determine a characteristic associated with the first section of the cable based on the vibration. The system may determine a location associated with the characteristic based on the cable map. The system may determine that the first section of the cable is associated with the location based on the location being associated with the characteristic. The system may associate the location and a length of a second section of the cable extending from an initial location to the location. The system may receive an input identifying the length of the second section of the cable and may output the location based on associating the location and the length of the second section of the cable.

Automated real-time characterization of resistance spot welds using ultrasound-based nondestructive evaluation requires a computational process and system to accurately and rapidly interpret the ultrasonic data in real time. Such a process can be automatically learned using artificial intelligence, from a dataset of exemplary ultrasonic data from nondestructive evaluation of resistance spot welds for which a corresponding ideal evaluation of each weld is provided. The process can then be implemented into a system to automatically interpret data from non-destructive evaluation in real-time. The ideal evaluation of each weld requires identification a large set of features that are observable in the ultrasonic signature and comprehensively characterize the corresponding weld process.

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.

A system comprising a computer readable storage device readable by the system, tangibly embodying a program having a set of instructions executable by the system to perform the following steps for detecting a sub-surface defect, the set of instructions comprising an instruction to receive scan data for a part from a transducer; an instruction to collect the scan data; an instruction to determine an indication in the scan data that indicates a distractor, wherein the indication is based on a learning phase module and an inference phase module that the processor uses to self-assess the indication; and an instruction to create a defect indication report.

A system for detecting a sub-surface defect comprising a transducer fluidly coupled to a part located in a tank containing a liquid configured to transmit ultrasonic energy, the transducer configured to scan the part to create scan data of the scanned part; a pulser/receiver coupled to the transducer configured to receive and transmit the scan data; a processor coupled to the pulser/receiver, the processor configured to communicate with the pulser/receiver and collect the scan data; and the processor configured to detect the sub-surface defect and the processor configured to have a sub-surface defect confidence assessment and a prioritization for further human evaluation.

A system and method apply an input noisy ultrasonic test (UT) scan image to an input layer of a convolutional neural network, generate a feature map using a convolutional layer, pool the feature map using a pooling layer, apply the pooled feature map to a fully connected layer, generate a de-noised UT scan image, and output the de-noised UT scan image from an output layer.

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.