A defect detection method and apparatus detecting a defect in an object. The method comprises: obtaining ultrasound scan data derived from an ultrasound scan of the object under consideration, the ultrasound scan data being in the form of a set of echo amplitude values representing the amplitude of echoes received from the object during ultrasound scanning at certain spatial and temporal points; processing the ultrasound scan data to remove echo amplitude values received after a predetermined threshold time; generating at least one image from the processed ultrasound scan data; subjecting each generated image to an automated defect recognition process to determine whether there is a defect in the portion of the object represented by the image; issuing a notification indicating whether or not a defect has been found; and, if a defect has been found, storing the result of the automated defect recognition process in a defect database.

A vibration detection device includes an A/D conversion unit for receiving a sine wave signal of an AE wave corresponding to vibration generated in a target machine from an AE sensor that detects the AE wave and converting the received sine wave signal into digital data, an extraction unit for extracting, from the digital data, a data point of a local maximum value for each cycle of the sine wave signal, and an output processing unit for outputting the data point extracted by the extraction unit and cycle data including data points with the number of points which can be recognized as a sine wave and including the data point of a local maximum value so that an output unit visibly outputs the data point and the cycle data.

A method of performing an automated non-destructive examination of a composite structure includes identifying surface damage on the composite structure, coupling an automated tap tester device to a surface of the composite structure at a location of the surface damage, and performing, with the automated tap tester device, a plurality of tapping impacts on the surface within a testing area that encapsulates the surface damage. The method also includes receiving a plurality of acoustic signals associated with the plurality of tapping impacts, modeling sub-surface damage associated with the surface damage based on an analysis of the plurality of acoustic signals.

The disclosed technology can include a system for monitoring and detecting a fault in a fluid storage tank. A sensor can be located in, on, or proximate the fluid storage tank and can be configured to detect waveforms produced by the fluid storage tank in response to strain. The sensor can convert such waveforms into electrical signals and transmit such electrical signals in the form of vibration data to a controller. The controller can compare the vibration data to stored data, and based on such comparison, determine if a fault is present in the fluid storage tank.

The current disclosure determines if structural faults exist and extracts geometric features of the structural faults from acoustic emission waveforms, such as crack length and orientation, and can evaluate the structural faults online, during normal operation conditions.

To provide a method for evaluating a corroded part, the method making it possible to specify only a waveform reflected by a corroded part and to evaluate the waveform. When a transmission unit (2) is moved on the surface of a metal pipe (60) and the distance between a corroded part (5) and the transmission unit (2) is changed, only a waveform portion A of ultrasonic waves reflected by the corroded part (5) moves toward the left or right along an X axis, and only the intensity of a noise waveform portion B included in a received wave changes upward or downward along a Y axis, which makes it possible to separate the waveform portion A and the noise waveform portion B of a longitudinal-wave surface wave reflected by the corroded part (5) and evaluate the waveform portion A in detail.

Described herein are systems, methods, and other techniques for determining a material type while an implement of a construction machine is interacting with a ground surface. A vibration signal that is indicative of a movement of the implement is captured. One or more features are extracted from the vibration signal. The one or more features are provided to a machine-learning model to generate a model output. The material type of the ground surface is predicted based on the model output.

A carpet recognition method for a robot cleaner. The robot cleaner comprises a sleeve and an ultrasonic sensor, wherein the ultrasonic sensor is fixed in the sleeve. The recognition method comprises: controlling the ultrasonic sensor to vertically transmit an ultrasonic signal to the current ground, and to receive an actual echo signal reflected by the current ground and determining whether the actual echo signal is different from the standard echo signal of the normal ground, and if so, recognizing the current ground as a carpet surface

An active mechanical waveguide including an ultrasonically-transmissive material and a plurality of reflection points defined along a length of the waveguide may be driven at multiple resonant frequencies to sense environmental conditions, e.g., using tracking of a phase derivative. In addition, frequency-dependent reflectors may be incorporated into an active mechanical waveguide, and a drive frequency may be selected to render the frequency-dependent reflectors substantially transparent.

A method for detecting and processing the carriageway condition of a carriageway on which a vehicle is driven, by means of at least one noise sensor provided on the vehicle, in particular by means of at least one mechanical vibration sensor, wherein noise signals travelling through the vehicle are sensed by a noise sensor and conclusions as to the carriageway condition are drawn from the sensed noise signals. According to said method, the section of route on which the vehicle is currently being driven is determined, the determined carriageway condition is assigned to the section of route, said section of route and the carriageway condition that has been determined and assigned to the section of route are transmitted to a computer network, in particular to a cloud-based computing service, and the information relating to the carriageway condition assigned to a section of route is made available via the computer network.