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.

A method of examining the integrity of an aircraft structure including determining an electrical conductivity or resistivity of the metal matrix composite of the aircraft structure. An apparatus for performing such a method is also provided. A method of estimating damage in an aircraft structure and a method of estimating the remaining operational life of an aircraft structure are also provided.

Provided are a structure inspection method and a structure inspection system capable of easily detecting an abnormal location and inspecting an internal state of the abnormal location in detail. The structure inspection method includes: a step of capturing a thermal image of a surface of a structure with an infrared camera; a step of detecting a first region estimated to have an internal abnormality, on the basis of the thermal image; and a step of measuring an internal state of the first region in a case where the first region is detected. In the step of measuring the internal state of the first region, the internal state of the first region is measured by capturing an image that visualizes the internal state of the first region using an electromagnetic wave or an ultrasonic wave.

The present application describes a magneto-optical based guided waves system for inspection and monitoring of assets. The system has a magnetostrictive-based wave emitter for signal generation and a network of optical fiber sensors for detection, both mechanically coupled to the asset, a device with embedded software for hardware control and signal processing, and a framework capable of providing visual and analytic insights about the condition of the structure of interest. The enhancement of flaw detection, location and characterization abilities of the system are obtained through the use of high sensitivity and passive fiber optics sensors with very small size, distributed and multi-parameter sensing capabilities.

Disclosed herein is a system, apparatus and method directed to detecting damage to an optical fiber of a medical device. The optical fiber includes one or more core fibers each including a plurality of sensors configured to (i) reflect a light signal based on received incident light, and (ii) alter the reflected light signal for use in determining a physical state of the multi-core optical fiber. The system also includes a console having non-transitory computer-readable medium storing logic that, when executed, causes operations of providing a broadband incident light signal to the multi-core optical fiber, receiving reflected light signals, receiving reflected light signals of different spectral widths of the broadband incident light by one or more of the plurality of sensors, identifying at least one unexpected spectral width or a lack of an expected spectral width, and determining the damage has occurred to the optical fiber based on the identification.

A detection method of nonlinear ultrasonic guided wave with carrier modulation is described. The high and low frequency components are determined according to the frequency response characteristics of the detection object, and the high-frequency components are processed by delay and combined with the low-frequency components to form a carrier modulation signal. The single excitation and single receiving mode are adopted for signal acquisition. The carrier modulation signal with high frequency and low frequency components is excited by a single transducer. The nonlinear modulation effect is produced by the interaction between the carrier signal and the damage, and the signals are collected by the receiving transducer through transmission method. According to the arrival time of high frequency components and the time of end reflection echo, the signal is intercepted and analyzed. After filtering and normalization processing, the received signal is decomposed by empirical mode decomposition (EMD). According to the decomposed IMF spectrum information, IMF components including fundamental frequency and nonlinear frequency components are used for signal reconstruction. The difference frequency components generated by the modulation of high-frequency and low-frequency, namely nonlinear components, are extracted, and the non-linear coefficient is calculated. The damage degree of materials is evaluated based on the nonlinear coefficient of nondamaged state.

A deterioration diagnosis device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations include: storing deterioration degree histories for portions of a structure to be diagnosed; acquiring reference information related to deterioration of the portions; generating a deterioration prediction model for predicting the deterioration degrees of the portions based on the histories and the reference information; predicting the deterioration degrees of the portions at a prediction time by using the generated deterioration prediction model; selecting, from among the portions, a portion where the deterioration degree predicted at the prediction time meets a predetermined condition; and outputting information relating to the selected portion and the predicted deterioration degree of the selected portion.

A system for monitoring fatigue of a mechanical structure includes a memory storing response values each associated with one of predetermined stress ranges and one of predetermined mean stresses. Each response value expresses an upper limit for number of cycles of stress at a predetermined observation point of the mechanical structure in a situation where the cycles have the stress range and the mean stress related to the response value. The system includes processing equipment for updating, for each response value, a stress history value expressing number of cycles occurred in a time-trend of the stress and having the stress range and the mean stress related to the response value. A fatigue damage sum is updated based on the response values and the stress history values. The fatigue damage sum expresses cumulated fatigue damage of the mechanical structure and can be used for real-time fatigue monitoring.

Provided is an inspection support system capable of improving work efficiency or work accuracy in inspection of structures. A self-traveling apparatus autonomously travels in response to a first travel command based on a first inspection image to a target position, and shoots photographing targets captured in the first inspection image to acquire a second inspection image; and an information processing apparatus extracts, from among the photographing targets captured in the second inspection image, a matched target that matches the photographing target captured in the first inspection image, and correlates the identification information that identifies, in an identifiable manner, the photographing targets that are captured in the first inspection image and are matched to the matched target, with the matched target.

A method and system for easily predicting a remaining lifetime of a hose are provided. A hose remaining lifetime prediction method and a hose remaining lifetime prediction system, for predicting a remaining lifetime of a hose 1 in use, include obtaining in advance, for a hose of the same type, relationship between use time of an inner tubular rubber layer 11 at a reference temperature and a physical property value of rubber forming the inner tubular rubber layer 11, generating a thermal degradation model for the inner tubular rubber layer 11, calculating, for the hose 1 in use, reference temperature use time being use time of the inner tubular rubber layer 11 at the reference temperature until a time of the prediction, and predicting the remaining lifetime of the hose 1 in use, based on comparison between the reference temperature use time and the thermal degradation model.