Crack Detection Capability Research Articles

Internal inspection method for crack defects in ferromagnetic pipelines under remanent magnetization

Magnetic flux leakage (MFL) testing is effective in detecting corrosion in ferromagnetic pipelines. However, the MFL generated at the crack is highly weak and is easily drowned in the strong background magnetic field and difficult to detect. Therefore, this paper creatively proposes a remanent magnetic flux leakage (RMFL) detection method. This method utilizes the hysteresis state of the pipeline after MFL detection and identifies crack defects by collecting the remanent magnetic flux density near the inner wall surface of the pipeline. The remanent magnetic field distribution near the pipeline surface is examined at different crack sizes, magnetization speeds, and lift-off values via theoretical analysis, finite element simulation, and experimental verification. In addition, experiments have demonstrated that in terms of crack detection capability, RMFL is superior to MFL. Therefore, adding a RMFL measurement module to the conventional MFL detector has important practical value. It can realize the targeted collaborative application of these two technologies, namely the synchronous detection of corrosion and crack defects, which greatly improves the detection coverage and detection efficiency.

Generalized method for distributed detection and quantification of cracks in bridges

The development of a generalized machine learning approach based on distributed detection and quantification of cracks by optical fibers is described in this article. A Brillouin scattering optical fiber sensor system was employed to develop, test, and verify the method. The main components of the approach described herein consist of an unsupervised crack identification module based on the iForest algorithm and a crack quantification component by the one-dimensional convolutional neural network method. The main attribute of this model is the versatility for application in various types of structures. The proposed method does not require further application-dependent training or calibration as long as the structural applications employ the same optical fiber type and installation adhesives. The effectiveness of the proposed method was verified by two experiments involving a 15-m steel beam in the laboratory and monitoring a twin set of 332-m-long, five-span continuous box girder concrete bridges. Regarding crack detection capabilities, it was possible to detect 107 out of 112 cracks in the laboratory beam and 20 out of the 21 in the bridges. The resolution of crack opening displacements for the steel beam and concrete bridges were 20.6 and 21.7 µm, respectively. The verification experiments further indicated the generality of the approach in applications to various types of structures and materials.

A Pavement Crack Detection and Evaluation Framework for a UAV Inspection System Based on Deep Learning

Existing studies often lack a systematic solution for an Unmanned Aerial Vehicles (UAV) inspection system, which hinders their widespread application in crack detection. To enhance its substantial practicality, this study proposes a formal and systematic framework for UAV inspection systems, specifically designed for automatic crack detection and pavement distress evaluation. The framework integrates UAV data acquisition, deep-learning-based crack identification, and road damage assessment in a comprehensive and orderly manner. Firstly, a flight control strategy is presented, and road crack data are collected using DJI Mini 2 UAV imagery, establishing high-quality UAV crack image datasets with ground truth information. Secondly, a validation and comparison study is conducted to enhance the automatic crack detection capability and provide an appropriate deployment scheme for UAV inspection systems. This study develops automatic crack detection models based on mainstream deep learning algorithms (namely, Faster-RCNN, YOLOv5s, YOLOv7-tiny, and YOLOv8s) in urban road scenarios. The results demonstrate that the Faster-RCNN algorithm achieves the highest accuracy and is suitable for the online data collection of UAV and offline inspection at work stations. Meanwhile, the YOLO models, while slightly lower in accuracy, are the fastest algorithms and are suitable for the lightweight deployment of UAV with online collection and real-time inspection. Quantitative measurement methods for road cracks are presented to assess road damage, which will enhance the application of UAV inspection systems and provide factual evidence for the maintenance decisions made by road authorities.

Study on the method of establishing the probability of detection curve for ultrasonic detection on railway hollow axle cracks

Obtaining the ultrasonic probability of the detection (POD) curve of railway hollow axle cracks through experiments in an effective way has always been a pending issue. A new method of obtaining ultrasonic testing data for railway hollow axles crack is proposed, which can obtain a large amount of detection data from a small number of specimens. A new equivalent crack parameter which can characterize the location, area and direction of cracks is proposed, and the POD curve is analyzed by using this parameter, quantifying the crack detection capability of ultrasonic detection equipment for the first time. The range of POD for different crack depths under in-service detection methods was calculated using semi-circular crack and chordal crack as the critical state of semi-elliptical crack, respectively. When the crack depth reaches 10 mm, the POD can reach more than 80% regardless of the shape of the crack.

Crack Growth Monitoring with Structure-Bonded Thin and Flexible Coils.

Structural health monitoring with thin and flexible eddy-current coils is proposed for in situ detection and monitoring of fatigue cracks in metallic aircraft structures, providing a promising means of crack sizing. This approach is seen as an efficient replacement to periodic inspections, as it brings economic and safety benefits. As such, printed-circuit-board eddy-current coils are viable for in situ crack monitoring for multi-layer, electrically conductive structures. They are minimally invasive and could be attached to or embedded into the evaluated structure. This work focuses on the monitoring of fatigue crack growth from a fastener hole with structure-bonded, thin, and flexible spiral coils. Numerical simulations were used for optimization of the driving frequency and selection of crack-sensitive coil parameters. The article also demonstrates the fatigue crack detection capabilities using spiral coils attached to a 7075-T6 aluminum coupon.

Improved damage sensing with random/aligned carbon nanofiber in bulk epoxy and adhesive joints

AbstractIn situ health monitoring of polymeric materials and adhesive joints can be done by adding conductive carbon nanofillers that enable inherent piezoresistive sensing capability in nonconductive polymers. In this study, carbon nanofiber (CNF) was added to epoxy in bulk and adhesive form to investigate their potential applications as strain and crack detection sensors, respectively. Tensile specimens of CNF/epoxy nanocomposites with different filler concentrations were tested to study their strain sensing capability. The nanocomposites were further used as adhesive for sensing crack propagation in adhesive joints. CNFs alignment was carried out inside the adhesive by AC electric field to study the alignment effect on crack detection capability and fracture toughness of joints. Tensile specimens showed excellent strain sensing capability at CNF concentrations of 0.4, 0.8, and 1.2 wt%. However, the gauge factor was found to be highest at 0.4 wt% and decreasing further with increasing CNF content. The CNF addition was found to enhance the crack detection capability as well as the fracture toughness in adhesive joints. Further improvements in the crack detection capability and fracture toughness were observed in the case of aligned nanocomposite joints. However, the effect of alignment was observed to be more profound at lower CNF content.

Demonstration of model-assisted probability of detection framework for ultrasonic inspection of cracks in compressor blades

The fatigue crack detection capability of nondestructive testing was quantitatively evaluated using the model-assisted probability of detection (MAPOD) process. A framework based on conventional MAPOD with dimension reduction was studied. By conducting a metamodel-assisted sensitivity analysis, the dimension of the physics-based model was reduced. The factors selected based on the sensitivity analysis were partitioned for both computational and physical experiments. Subsequently a transfer function was constructed. Additionally, a linear model of the relationship between the ultrasonic signal and crack size was obtained and calibrated by the transfer function by conducting physical experiments. The probability of detection curve and the corresponding detectable crack size were evaluated using the calibrated model. Furthermore, the improved MAPOD process was demonstrated by the fatigue crack detection in a J85 engine compressor first-stage blade tang hole.

Improvement of the computational efficiency of metaheuristic algorithms for the crack detection of cantilever beams using hybrid methods

This study examines the capability of various optimization algorithms and proposes novel hybrid algorithms for more precise prediction of open-edge cracks in cantilever beams. The natural frequencies of the beam with a crack are obtained by modal analysis and experimentally validated by impact testing. The performance of Harris hawk optimization (HHO), electrostatic discharge algorithm (ESDA), pathfinder algorithm (PFA) and Henry gas solubility optimization (HGSO) algorithms from the literature is evaluated to determine the location and depth of an open-edge crack for an Euler–Bernoulli beam. Then, hybrid algorithms (HHO-NM, ESDA-NM and PF-NM) are proposed to improve the results of the aforementioned algorithms. Simulation results show that the proposed hybrid algorithms yield much more precise results with fewer function evaluations than the previously introduced algorithms and, therefore, have superior crack detection capability. Statistical post hoc analysis shows that the proposed hybrid algorithm can be considered a high-performance algorithm, which can significantly improve the efficiency of crack detection applications.

A cylindrical ring dielectric resonator based passive wireless sensor for position insensitive crack monitoring

Passive wireless sensors enabled by the electromagnetic (EM) resonator inherit the merits of low cost and green in power consumption. The uncertainty of the crack location, however, will cause a sensitivity variation, which is tightly related to the operating mode and field distribution. A cylindrical ring dielectric resonator (CRDR) is proposed to cope with this issue in this paper. According to the shape perturbation of the EM resonator, the presence of the crack can cause an increase in the stored magnetic energy over the stored electric energy inside the crack, resulting in a resonant frequency shift to the lower frequency region. By comparing with cylindrical dielectric resonator (CDR), the sensing principle and crack detection capability based on the proposed CRDR is demonstrated in both simulation and experiment. The information gathered from this type of sensor can be used to support predictive maintenance, repair, and decision making for structural health monitoring (SHM) in extreme environments.

Modeling of microscope images for early detection of fatigue cracks in structural materials

From the perspectives of health monitoring and life extension of structural materials, this paper addresses the problem of early detection of fatigue cracks in metallic materials (e.g., polycrystalline alloys). To this end, optical images have been collected from an ensemble of test specimens to construct computationally efficient models of crack evolution; these images are segmented into two major categories. The first category comprises images of (structurally) healthy specimens, while the second category contains images of specimens with cracks, including those in early stages of crack evolution. Based on this information, algorithms for early detection of crack formation are formulated in the setting of image classification, where the bag-of-words (BoW) technique has been used to develop models of the sensed images from a microscope, resulting in computationally efficient crack detection algorithms. To evaluate the performance of these crack detection algorithms, experiments have been conducted on a special-purpose fatigue testing apparatus, equipped with a computer-controlled and computer-instrumented confocal microscope system. The results of experimentation with multiple test specimens show excellent crack detection capabilities when the proposed BoW-based feature extraction is combined with quadratic support vector machine (QSVM) for pattern classification. Comparative evaluation with other classification tools establishes superiority of the proposed BoW/QSVM technique.

A novel pulsed eddy current method for high-speed pipeline inline inspection

High-speed inspection of inner diameter (ID) and outer diameter (OD) surface defects on thick-wall steel pipes is an important aspect to advance the pipeline inline inspection (ILI) in the oil and gas industry. The state-of-the-art ILI methods including magnetic flux leakage (MFL), ultrasonic testing (UT), electromagnetic acoustic transducer (EMAT), eddy current (EC) and pulsed eddy current (PEC) are hardly applicable to practical high-speed pipeline ILI due to the reasons of long inspection time required for sensor response and low detection sensitivity caused by severe motion. New sensing techniques that offer faster inspection speed, deeper signal penetration depth, better detection sensitivity and linearity, as well as capability of ID/OD discrimination are imperatively needed. This paper proposes a novel PEC sensing method to detect and discriminate ID/OD defects by utilizing the conductivity-dependent and permeability-dependent distribution patterns of induced eddy current at the ID surface of steel pipes. For ID defects, the pattern is caused by the discontinuous conductivity distribution, while for OD defects, the pattern is caused by the non-uniform incremental permeability distribution. A pulse-excited current with short width (2.5 μs), low duty cycle (1%) and fast-falling edge (100 ns) is injected into an excitation coil, so that the secondary magnetic field at the fast-falling edge will produce a transient oscillation in a pair of differential pick-up coils. Then, the time-domain transient oscillatory pick-up signal is extended, filtered, amplified, and extracted to be one feature by an envelope detector and an average sample method, which is processed in real-time by the developed probe for facilitating the back-end data recording. Meanwhile, a novel high-speed pipeline inspection gauge (PIG) with a sensor array is developed for field testing to validate the effectiveness of the proposed PEC method that achieved high inspection speed, deep detection depth, superior sensitivity, good linearity, low power consumption, easy implementation, ID/OD discrimination and crack detection capability.

Aligning carbon nanofibres in glass-fibre/epoxy composites to improve interlaminar toughness and crack-detection capability

An electric field is used to align carbon nanofibres (CNFs) in the matrix of a glass-fibre reinforced-polymer (GFRP) composite to simultaneously improve the (a) delamination toughness, (b) electrical conductivity, and (c) damage-sensing capability. The CNFs are added to the epoxy resin prior to the manufacture of the GFRP composites. To align the CNFs, an alternating current (AC) electric field of 30 V/mm at 10 kHz is applied across the GFRP sheet throughout the matrix-curing process. The electromechanical force induced by the electric field, applied in the through-thickness direction of the composite sheet, rotates and aligns the CNFs in the direction of the applied electric field prior to the gelation of the epoxy matrix. After curing, the resultant aligned, ‘chain-like’, microstructure of the CNFs in the epoxy matrix significantly enhances both the interlaminar fracture toughness and the through-thickness electrical conductivity of the GFRP composite. Specifically, the addition of 0.7 vol% of randomly-orientated CNFs in the GFRP composite yielded an ∼50% and 25% increase in the values of the mode I fracture toughness pertinent to the initiation, GIci, and steady-state growth, GIcss, of delamination crack, respectively, compared to the control GFRP composite. The alignment of the CNFs, in the transverse direction to the direction of the crack growth, increases the mode I toughness values of GIci and GIcss by ∼100% and ∼80%, respectively, compared to the control GFRP composite. These significant increases are attributable to multiple toughening mechanisms, including debonding of the CNFs from the matrix, void growth of the epoxy matrix, pull-out and rupture of the CNFs. Further, the electric-field induced alignment of the CNFs, in the through-thickness direction, increases the out-of-plane electrical conductivity of the GFRP by about twenty-six times, compared to the GFRP composite containing randomly-orientated CNFs. Of particular interest, the damage-sensing capacity is enhanced for the GFRP composite with aligned CNFs in the epoxy matrix, which stems from the greatly increased out-of-plane electrical conductivity, as confirmed by a modelling study. Therefore, this present work has identified a new strategy to develop GFRP composites with greatly improved delamination toughness, electrical conductivity, and higher crack-detection sensitivity.

Non-destructive thermal-wave-radar imaging of manufactured green powder metallurgy compact flaws (cracks)

Thermal wave radar imaging (TWRI) was developed to detect manufacturing cracks in automotive powder metallurgy components (transmission sprockets) in their green (unsintered) state. The crack detection capability of the TWRI phase was validated by two sets of cracked/crack-free green and sintered sprockets which were sectioned after TWRI measurements. An automatic defect recognition (ADR) TWR image processing method was also developed to differentiate cracks from local defects. Measurement results demonstrated that TWRI is superior to conventional lock-in thermography imaging (LITI) in both flaw detection resolution and speed, and thus is a viable green-sprocket manufacturing flaw imaging technology.

Health Monitoring of the Aircraft Structure during a Full Scale Fatigue Test with Use of Resistive Ladder Sensors

The paper presents an application of resistive ladder sensors (or resistive crack gages) for health monitoring of an aircraft structure. An experiment was conducted during a Full Scale Fatigue Test (FSFT) of PZL 130 ORLIK TC II turbo-prop military trainer. The sensor can be successively used to detect and quantify fatigue cracks. It is a parametric transducer, similar to foil strain gages. Differences in shape of the measuring grid can be noticed. Principle of operation, sensor’s characteristics as well as block diagram of measurement system is presented in the paper. Also examples, both laboratory and FSFT data are delivered to prove surface crack detection capabilities. Simultaneously with new data collection, some investigation is carried out, connected with customize hardware elaboration, signal processing and structure-sensor integration method, what is briefly described.

Composite Fracture Detection Capabilities of FBG Sensor and AE Sensor

Non-destructive testing methods of composite materials are very important for improving material reliability and safety. AE measurement is based on the detection of microscopic surface movements from stress waves in a material during the fracture process. The examination of AE is a useful tool for the sensitive detection and location of active damage in polymer and composite materials. FBG (Fiber Bragg Grating) sensors have attracted much interest owing to the important advantages of optical fiber sensing. Compared to conventional electronic sensors, fiber-optical sensors are known for their high resolution and high accuracy. Furthermore, they offer important advantages such as immunity to electromagnetic interference, and electrically passive operation. In this paper, the crack detection capability of AE (Acoustic Emission) measurement was compared with that of an FBG sensor under tensile testing and buckling test of composite materials. The AE signals of the PVDF sensor were measured and an AE signal analyzer, which had a low pass filter and a resonance filter, was designed and fabricated. Also, the wavelength variation of the FBG sensor was measured and its strain was calculated. Calculated strains were compared with those determined by finite element analysis.

Detecting cracks in aircraft engine fan blades using vibrothermography nondestructive evaluation

Inspection is an important part of many maintenance processes, especially for safety-critical system components. This work was motivated by the need to develop more effective methods to detect cracks in rotating components of aircraft engines. This paper describes the analysis of data from vibrothermography inspections on aircraft engine turbine blades. Separate but similar analysis were done for two different purposes. In both analyses, we fit statistical models with random effects to describe the crack-to-crack variability and the effect that the experimental variables have on the responses. In the first analysis, the purpose of the study was to find vibrothermography equipment settings that will provide good crack detection capability over the population of similar cracks in the particular kind of aircraft engine turbine blades that were inspected. Then, the fitted model was used to determine the test conditions where the probability of detection (POD) is expected to be high and probability of alarm is expected to be low. In our second analysis, crack size information was added and a similar model was fit. This model provides an estimate of POD as a function of crack size for specified test conditions. This function is needed as an input to models for planning in-service inspection intervals.

Infrared thermography as a new method for quality control of sheet metal parts in the press shop

Active dynamic thermography was proved to be a new alternative for crack detection. To achieve zero failure sheet metal production in the press shop, this method was investigated for different steel components, which were directly collected from series production. And the cracks in the presented specimens differ in length and in depth, and a series study on these surface cracks is executed. The crack detection capabilities of active thermography with two different excitation sources were compared: optical and inductive excited thermography. Results of experiments obtained by these two techniques are showed and compared with each other. Opportunities of applying these methods in press shop are discussed.

AE 기법을 이용한 외부수압을 받는 복합재 원통의 균열 검출

복합재료의 비파괴 검사 방법에 관한 연구는 복합재료의 신뢰성 및 안전성을 개선시키는데 매우 중요하다. AE기법은 소성변형, 섬유파손, 모재균열 또는 박리에 의해 생성되는 탄성파에서 감지되는 변형 에너지를 감지하여 결함을 평가하는 기법이다. 본 논문에서는 외부 수압을 받는 필라멘트 와인딩 복합재 원통과 샌드위치 원통의 수압시험이 수행되었고, PVDF센서로부터 측정된 AE 신호특성을 분석하였다. 또한, L-C 동조회로와 Band-pass filter 회로를 가진 AE 신호분석기를 설계, 제작 하였으며, 필라멘트 와인딩 원통과 샌드위치 원통에 대한 수압실험에서 제작된 AE 신호분석기의 균열 검출 능력을 평가하였다. The studies on the non-destructive testing methods of the composite materials are very important for improving their reliability and safety. AE(Acoustic Emission) can evaluate the defects by detecting the emitting strain energy when elastic waves are generated by the generation and growth of a crack, plastic deformation, fiber breakage, matrix cleavage or delamination. In this paper, the AE signals of the filament wound composite cylinder and sandwich cylinder during the pressure test were measured and analyzed. The signal characteristics of PVDF sensors were measured, and an AE signal analyzer which had the band-pass filter and L-C resonance filter were designed and fabricated. Also, the crack detection capability of the fabricated AE signal analyzer wes evaluated during the pressure tests of the filament wound composite cylinder and the sandwich cylinder.

In-Situ Crack Detection Using Thermo Elastic Stimulated Lock-In Thermography

Abstract In this work the lock-in-thermography was tested for crack detection capabilities. Instead of an external heat source the thermo elastic effect induced by the mechanical loading is used for cyclic thermal stimulation. This procedure was applicable for crack detection in an aluminium centre cracked tension specimen (CCT) during a running fatigue test. As lock-in input signal the force as well the signal of a strain gauge, placed in the surrounding of an estimated crack initiation site, can be used, respectively. This strain gauge can also be attached perpendicular to the loading direction.

Real time crack detection using mountable comparative vacuum monitoring sensors

Current maintenance operations and integrity checks on a wide array of structures require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be disassembled and removed or personnel must be transported to remote locations. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board health monitoring systems were used to more regularly assess structural integrity. A research program has been completed to develop and validate Comparative Vacuum Monitoring (CVM) Sensors for surface crack detection. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Detection (POD) levels for a wide array of application scenarios. Multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual flight environments, and the prospects for structural health monitoring applications on aircraft and other civil structures.