Certain embodiments may generally relate to structural damage detection. An embodiment may be directed to method for identifying a presence and a location of structural damage. Such method may include training a convolutional neural network (CNN) for a joint of a structure, sending instructions to a modal shaker to induce an input to the structure, receiving, as a result of the induced input, a raw acceleration signal at the joint, computing, based on the trained CNN and the raw acceleration signal, an index value of the joint, and identifying, according to the index value, a presence of a location of structural damage of the structure. In a further embodiment, the index value represents a likelihood of damage at the joint.

Methods, systems, and apparatuses are provided for detecting and determining conditions of and conditions within a fluid conduit.

A sampling device receives, from a transducer computing device located within a predefined proximity to an equipment in an operating environment, a vibration sample from the operating environment and increments a retrain counter. In response to determining that the incremented retrain counter does not meet or exceed a retrain threshold, the sampling device predicts, using a model, an anomalous or non-anomalous designation for the vibration sample and a cluster assignment, to a particular cluster of a set of clusters, for the vibration sample when the model predicts the non-anomalous designation for the vibration sample. The sampling device receives a subsequent vibration sample and further increments the retrain counter. In response to determining that the further incremented retrain counter exceeds a retrain threshold, the sampling device receives a subsequent set of vibration samples and retrains, using the subsequent vibration sample and the subsequent set of vibration samples, the model.

Methods for performing maintenance operations using unmanned aerial vehicles (UAVs). The methods are enabled by equipping a UAV with a maintenance tool capable of performing a desired maintenance operation (e.g., nondestructive inspection) on a limited-access surface of a large structure or object (e.g., a wind turbine blade). The UAV uses re-orientation of lifting means (e.g., vertical rotors) to move the maintenance tool continuously or intermittently across the surface of the structure while maintaining contact with the surface of the structure undergoing maintenance.

The present invention relates generally to a system and method for measuring the structural characteristics of an object. The object is subjected to an energy application processes and provides an objective, quantitative measurement of structural characteristics of an object. The system may include a device, for example, a percussion instrument, capable of being reproducibly placed against the object undergoing such measurement for reproducible positioning. The system includes features for adjusting the energy applied to an energy application tool to compensate for the physical characteristics or type of the object, and/or for orientation of the device relative to the horizontal during measurement. The system also includes a disposable feature or assembly for minimizing cross-contamination between tests. The structural characteristics as defined herein may include vibration damping capacities, acoustic damping capacities, structural integrity or structural stability.

A sensor unit includes an orientation sensor, an electronic processor coupled to the orientation sensor, and memory coupled to the electronic processor and storing support structure configuration data and instructions. The instructions, when executed by the electronic processor, cause the sensor unit to monitor a position of a conductor support structure associated with the sensor unit based on data from the orientation sensor and generate an alert message responsive to determining that the position violates a position threshold. The position threshold is generated based on the support structure configuration data.

A system and method to assess utility pole integrity, by using existing telecom fiber optic cable as a sensor cable, instant mechanical impact on the pole(s), DAS technology and a machine learning model. An instant mechanical impact creates a vibration event on the optical fiber cable mounted/suspended on a target pole, which is detected/recorded by DAS. By applying a machine learning model on the DAS signals, the target pole's integrity condition is obtained.

A drone system for collecting structural condition data about a structure having an array of sensors disposed at various locations on the structure and methods of using such a drone system are disclosed herein. The drone inspection system leverages neural networks to calculate a drone flight path to classify the location of passive sensors and calculate a drone flight path to collect structural condition data about the structure using line of sight sensors for digital twin generation. Some of the sensors disposed on the structure may be passive sensors that comprise energy harvesters and must be energized to report the structural collection data to the drone. The drone inspection system may comprise an energy transfer module for energizing the passive sensor via the energy harvester.

Systems and methods include reception, for each of a first plurality of consecutive time periods, of an acceleration value associated with a first location of a structure, determination of a first value of a first indicator based on absolute values of the acceleration values, determination of a second value of a second indicator based on absolute values of differences of consecutive one of the acceleration values, determination of a first value of an index based on the first value and the second value, determination of a physical characteristic of the structure based on the first value of the first indicator and the first value of the index, and transmission of an alert based on the physical characteristic.

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.