A system for non-destructive testing of a bond condition of concrete beams reinforced by steel rods is described. The system includes a transducing transmitter, a transducing receiver, and an ultrasonic pulse generator configured to generate drive signals for the transducing transmitter and receive a plurality vibrational waves at the transducing receiver. The system further includes a computing device including a measurement circuit configured to record a transit time for each vibrational wave and divide a distance between the transducing transmitter and the transducing receiver by the transit time to determine a pulse velocity of each vibrational wave, a comparison circuit configured to identify a highest pulse velocity of the vibrational waves and compare each highest pulse velocity to a first reference pulse velocity, and a decision circuit including an artificial neural network configured to identify a compromised bond condition around a steel rod.

A method according to one embodiment includes receiving sensor data from a plurality of sensors of a door device associated with a door, analyzing the sensor data to determine behavior data indicative of a behavior of the door device, and comparing the behavior data to a plurality of representative data associated with a plurality of door faults to determine a corresponding likelihood that the sensor data corresponds with each of the door faults.

In a method for detecting rail fracture, vibration data generated according to train operation is inputted, and the vibration data is collected using a distributed acoustic sensing (DAS) system. The inputted vibration data is imaged into the relationship between time and frequency. The imaged image is learned. Rail fracture of train is decided from the imaged vibration data, based on the learning.

The present disclosure are directed to techniques for defect detection and identification inside batteries. In one aspect, a non-invasive method of identifying and labeling defects in a battery cell includes transmitting acoustic signals through a battery cell via one or more first transducers, receiving response signals in response to the acoustic signals at one or more second transducers, determining whether at least one feature of interest exists in the battery cell based on analyzing the response signals, performing an identification and labeling process on the at least one feature of interest to determine at least one defect in the battery cell, and outputting a result of the identification and labeling process.

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.

A tunnel defect detection and management system based on a vibration signal of a moving train. This system identifies the defects in a subway tunnel structure and soil behind a wall through the acquisition, transmission and analysis of an on-board acceleration signal. A signal acquisition sensor is mounted on the moving train. A signal acquisition module and a signal transmission system are mounted in the train to preprocess and compress the signal. A data processing and analysis server performs data analysis to quickly identify the defects of the tunnel and the auxiliary structure thereof, and determine a defect location and type. A tunnel management platform releases real-time detection information and health status of the tunnel, alarms for the defects, and releases the defect data to relevant personnel to take measures.

A system for monitoring rotating equipment. The system includes a sensor device that acquires vibration data, acoustic emission data, temperature data, and magnetic flux data of the rotating equipment. The sensor device includes base, holding frame, first integrated circuit, housing, and power source. The first integrated circuit includes a plurality of sensors and a microcontroller configured to receive vibration data, acoustic emission data, temperature data, magnetic flux data from plurality of sensors and determine anomalies of the rotating equipment. The system further comprises an application server that receives vibration data and magnetic flux data, determines revolutions per minute (RPM) data for rotating equipment, and diagnose faults based on processed vibration data and RPM data. The application server further generates a set of features and corresponding feature values and analyzes them to diagnose faults, and predict remaining useful life of the rotating equipment.

A computerized method of image processing using processing circuitry to apply a previously trained machine learning model in a system for non-destructive testing (NDT) of a material is described. The method can include acquiring acoustic imaging data of the material, the acoustic imaging data acquired at least in part using an acoustic imaging modality, generating an acoustic imaging data set corresponding to an acoustic propagation mode, applying the previously trained machine learning model to the acoustic imaging data set, and generating an image of the material depicting a probability of a flaw per pixel or voxel based on the application of the previously trained machine learning model.

A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

A method for non-destructive testing of a quality of an ultrasonic weld from a welding process includes detecting of a time-dependent measurement value over a period of time, where the measurement value is characteristic of a mechanical or electrical vibration behavior of a welding process to be tested. The method includes evaluating a measurement-value course of the detected time-dependent measurement value by using a Fourier analysis. The method further includes comparing a result of the evaluation to a reference value in order to test the quality of the weld. A measuring device and an ultrasonic welding system are also included.