Pencil Lead Break Tests Research Articles

A Deep Learning Framework for Acoustic Emission Sources Localization and Characterization in Complex Aerospace Panels

This paper presents a data-driven approach based on deep stacked autoencoders for the localization and characterization of acoustic emission sources in complex aerospace panels. The approach leverages the multimodal and dispersive reverberations of acoustic emissions. The approach is validated by Hsu-Nielsen pencil lead break tests on a fuselage section of a Boeing 777 instrumented with a single piezoelectric sensor.

Differences in the Acoustic Emission Characteristics of 345 MPa Normal-Strength Steel and 460 MPa High-Strength Steel

This study characterized 345 MPa normal-strength steel (NSS) and 460 MPa high-strength steel (HSS) with the acoustic emission (AE) technique. Pencil lead break (PLB) and tensile tests were performed, and the generated AE signals were recorded. The AE signals from the PLB tests were converted into the frequency and time-frequency domains via a fast Fourier transform (FFT) and wavelet transform. The AE signals generated during the tensile process were divided into four stages for analysis. The AE parameters, such as the counts, amplitude, hit and duration, were extracted from the signals. An FFT was conducted to obtain the features of frequency spectrum at each stage. The comparisons revealed the similarities and differences in the AE characteristics between the two steels. It was found that the energy of the AE signals for the 460 MPa HSS was less overall than that of the 345 MPa NSS. The analysis results indicated that the AE technique is a method that can potentially be used to distinguish the two steels, and additional studies for accurate identification are needed. As 460 MPa HSS is being widely used but related AE studies are limited, this study provides data for additional investigations of the damage in HSS structures with the AE method.

Velocity-Free Localization of Autonomous Driverless Vehicles in Underground Intelligent Mines

The rapid progress of science and technology has created favorable conditions for the development of autonomous driverless vehicles. However, the complex conditions in the cities greatly limit the application and promotion of autonomous driving technology. To solve the problem, the underground mine is recommended to apply as a pilot for the promotion of autonomous driverless vehicles. The autonomous vehicles are all connected to the internet to form a vehicle-to-vehicle (V2V) system. Because of the multi-levels and multi-stopes in underground mines, the key point is to obtain the precise locations of the vehicles in real-time. Therefore, the microseismic monitoring, image recognition technology, artificial intelligence training, and smart sensors are comprehensively utilized, based on the internet of things and cloud computing, to locate the autonomous driverless vehicles. Virtual sources localization and pencil lead break tests are conducted to simulate and eliminate the effectiveness and accuracy of the velocity-free localization method. Virtual sources localization demonstrate that the velocity-free localization method is accurate and reliable. Results of pencil lead break tests show that the average locating errors of X coordinates, Y coordinates, and absolute distance are 0.4318 cm, 0.1136 cm, and 0.5188 cm, respectively. The application and promotion of the autonomous driverless vehicles in underground mines can not only solve the problems of deep mining and reduce the frequent disasters caused by the harsh conditions but also can protect the life and property of the workers, as well as provide technical support for the safe and efficient recycling of deep resources.

냉간압연률이 다른 STS316L의 탄성파 특성

In this study, the elastic wave was detected by the pencil lead break and tensile test using a stainless steel sheet with a thickness of 1 mm transformed by cold rolling. The detected elastic wave was analyzed by the time-frequency analysis method using Labview. The elastic wave detected from the pencil lead break and tension could be characterized by time-frequency analysis using Labview. As the degree of cold rolling increased, the amount of martensite increased and the dominant frequency decreased.

Feasibility of studying 460 MPa high-strength steel affected by strain aging with acoustic emission method

Strain aging significantly influences the behavior of partially damaged structural steel. The effects of strain aging can be determined based on mechanical experiments, but the sampling process causes further damage to the structures. To explore the possibility of using nondestructive testing (NDT) technology to distinguish the strain aging effects on steel materials, this study investigates the characteristics of acoustic emission (AE) signals for high-strength steel (HSS) affected by strain aging. First, different strain aging effects were applied to 460 MPa HSS specimens. Second, pencil lead break (PLB) tests were performed on the specimens with strain aging, and the generated AE signals were recorded. Finally, tensile tests were conducted, that the strain aging effects on mechanical behavior were determined. The obtained AE signals were compared by extracting the AE parameters and analyzed in the frequency and time-frequency domains with a fast Fourier transform (FFT) and a wavelet transform (WT), respectively. The study shows that the strain aging effects change the characteristics of the AE signals. Compared to the prestrain, the aging time has a more pronounced impact on the AE behaviors. This research proposes a possible NDT method to determine the effects of strain aging on steel materials. Experimental data are provided to detect the degree of partial damage to 460 MPa HSS owing to strain aging using the AE method.

Towards a better understanding of the CMUTs potential for SHM applications

The ability of capacitive micromachined ultrasonic transducer (CMUTs) to design broadband sensors for structural health monitoring (SHM) is studied through both multi-frequency and bandwidth aspects. Elementary cells are composed of circular membranes fabricated using the standard MUMPS Process. The multi-frequency aspect, which involves different individual membranes from 50 to 250 μm radius, is theoretically addressed through a numerical modeling. The targeted frequency range, consistent with the SHM application, is then between 80 kHz and 2 MHz. Geometrical features induced by the manufacturing process greatly affect the dynamic properties of the membranes and this is experimentally validated. The bandwidth aspect is also addressed on an array of identical 100 μm radius membranes thus involving their intrinsic capabilities. Harmonic excitation with targeted frequencies 300, 530 and 800 kHz, below and beyond the resonance frequency of the membranes, are performed. The influence of the bias voltage VDC on the signal-to-noise ratio is studied according to the excitation frequency. As a result, a signal-to-noise of 20 dB is achieved around the resonance frequency. Finally, the circular membranes array is tested for acoustic emission sensing through a pencil lead break test. In spite of a low signal-to-noise ratio, acoustic events are clearly detected. The multi-frequency aspect and the large bandwidth capability of the CMUTs are hence demonstrated and highlight the adaptability of the sensor to its environment.

Fractional Fourier Transform Based Adaptive Filtering Techniques for Acoustic Emission Signal Enhancement

Acoustic emission technique (AET) is a well-known non-destructive testing method used for the detection of crack growth and monitoring structural integrity of components. The main limitation in the application of AET is the fact that acoustic emission (AE) signal is often affected by background noise. A new approach is proposed in this paper to reduce the background noise and to improve the accuracy of AE signal detection. Various noise reduction methods have been applied and studied for AE signal enhancement so far. Some of the studies proved that self-adaptive noise cancellation (SANC) and adaptive noise cancellation (ANC) are better techniques for denoising the AE signal. This paper proposes the use of ANC and SANC schemes based on Fractional Fourier Transform (FrFT) for AE signal enhancement. FrFT is the generalization of the classical Fourier Transform (FT). The use of FrFT gives an advantage of an additional degree of freedom (angle of rotation in time–frequency plane) over the traditional Fourier transform and could provide improved performance. In this method, the noisy signal is rotated in time- frequency plane to extract the signal in Fractional Fourier domain (FrFD). Two adaptive filters viz. least mean squares and normalized least mean squares are studied for FrFD based ANC approach. The performance of the proposed method is validated using real AE signals acquired from three different experiments: pencil lead break test, composite drilling test and concrete compression test. Results are compared with the time and frequency domain adaptations. Moreover, the results have also been compared with the frequency domain adaptation techniques. Results have demonstrated that FrFD based ANC approach outperforms the conventional time domain and frequency domain adaptive filtering with an improved signal to noise ratio and significantly reduces mean square error.

Backing layers on electroacoustic properties of the acoustic emission sensors

The acoustic emission sensors were tailored by using Piezoelectric Lead Zirconate Titanate (PZT) ceramic as sensing element and different materials as backing layers. The influences of backing layers on electroacoustic properties of the sensors were investigated based on the pencil lead break test and simulated noise experiments. The results show that compared with PZT ceramic, the resonant frequency of the acoustic emission sensors decreases due to damping effects from backing layer and the metal shell. The backing layer of air has the largest acoustic reflection coefficient, and epoxy resin/tungsten powder has the largest acoustic attenuation performance, which is benefit to improve the bandwidth of the acoustic emission sensor. The acoustic emission sensors made of different backing layers own good reception ability to acoustic emission signals, and the characteristic parameters analysis results prove their good identifying ability to effective acoustic emission signals and noise signals.

A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels

Abstract This paper introduces a deep learning-based framework to localize and characterize acoustic emission (AE) sources in plate-like structures that have complex geometric features, such as doublers and rivet connections. Specifically, stacked autoencoders are pre-trained and utilized in a two-step approach that first localizes AE sources and then characterizes them. To achieve these tasks with only one AE sensor, the paper leverages the reverberation patterns, multimodal characteristics, and dispersive behavior of AE waveforms. The considered waveforms include AE sources near rivet connections, on the surface of the plate-like structure, and on its edges. After identifying AE sources that occur near rivet connections, the proposed framework classifies them into four source-to-rivet distance categories. In addition, the paper investigates the sensitivity of localization results to the number of sensors and compares their localization accuracy with the triangulation method as well as machine learning algorithms, including support vector machine (SVM) and shallow neural network. Moreover, the generalization of the deep learning approach is evaluated for typical scenarios in which the training and testing conditions are not identical. To train and test the performance of the proposed approach, Hsu-Nielsen pencil lead break tests were carried out on two identical aluminum panels with a riveted stiffener. The results demonstrate the effectiveness of the deep learning-based framework for single-sensor, AE-based structural health monitoring of plate-like structures.

A Fast Beamforming Method to Localize an Acoustic Emission Source under Unknown Wave Speed.

The beamforming method is capable of localizing the acoustic emission source in a large-scale structure but its accuracy relies strongly on the assumed propagation speed and it is quite time consuming to apply in online monitoring. This paper proposes a fast beamforming method to localize an acoustic emission source in a thin-walled structure with unknown wave speed. Firstly, the Bartlett beamforming method (BBM) is introduced into broadband Lamb wave signal processing to develop an L-shape array-based damage source localization method for a thin-walled structure. Secondly, the fast Bartlett beamforming method (FBBM) is proposed based on the characteristics of BBM. Finally, the pencil-lead break test is carried out to validate the proposed method. The test results show that the FBBM can accurately localize the damage source by any given probable wave speed much more rapidly than traditional delay-and-sum beamforming.

Application of Bayesian estimation to structural health monitoring of fatigue cracks in welded steel pipe

Abstract Vibration induced fatigue is a well-known problem in oil and gas piping systems. However the use of vibration data to detect damage is not an easy task without a priori knowledge of the undamaged condition. In this paper, level 1: Detection and level 2: Localization of damage from structural health monitoring strategies is adapted for damage identification. An experimental trial of acoustic emission monitoring was run to monitor fatigue damage during a full-scale resonance fatigue test of a girth-welded steel pipe from healthy condition until failure. The pipe was excited into the first mode of vibration using a resonance fatigue testing machine in order to determine the high-cycle fatigue strength of the weld. The information provided by acoustic emission monitoring is useful in evaluating the condition of the pipe during the test and the occurrence of cracking before failure. However, the acoustic emission signals are embedded in noise. To overcome this problem, the signals from different combinations of sensors were recursively cross-correlated, which provides for the derivation of a new effective coefficient (EC) parameter for Bayesian estimation. This parameter is useful for evaluating uncertainty arising from the signals that contribute to source localization errors. The estimation finds the most probable parameters corresponding to cracks using prior knowledge derived from standard pencil lead break tests. The proposed method demonstrates a strong relationship between the acoustic emission energy and the estimated coefficients. A high correlation between signals was found to be associated with cracking, and a low correlation between signals was found to be associated with random signals or noise. The method will be useful for monitoring the condition of piping to manage the risk of vibration induced fatigue failure.

Single-Sensor Acoustic Emission Source Localization in Plate-Like Structures Using Deep Learning

This paper introduces two deep learning approaches to localize acoustic emissions (AE) sources within metallic plates with geometric features, such as rivet-connected stiffeners. In particular, a stack of autoencoders and a convolutional neural network are used. The idea is to leverage the reflection and reverberation patterns of AE waveforms as well as their dispersive and multimodal characteristics to localize their sources with only one sensor. Specifically, this paper divides the structure into multiple zones and finds the zone in which each source occurs. To train, validate, and test the deep learning networks, fatigue cracks were experimentally simulated by Hsu–Nielsen pencil lead break tests. The pencil lead breaks were carried out on the surface and at the edges of the plate. The results show that both deep learning networks can learn to map AE signals to their sources. These results demonstrate that the reverberation patterns of AE sources contain pertinent information to the location of their sources.

Design and Development of Signal Conditioning Unit for Acquisition of Acoustic Emission Signal for Metallic Materials

This paper presents the details of design and development of a signal conditioning unit (SCU) for the acquisition of acoustic emission (AE) signals from metallic materials. Acoustic emission is the phenomenon of sound wave generation in materials undergoing deformation and fracture processes. The proposed SCU is a basic building block employed to interface a wide range of sensor signals to provide an optimal level for the next stage processing and to analyze. The piezoelectric sensor is used for the detection of AE signals in this development. An integrated prototype SCU has been fabricated, assembled and validated in the laboratory by acquiring AE signals generated during pencil lead break tests on a stainless steel plate. The performance of the in-house developed SCU has been compared with the commercially available one, and the results are presented in this paper.

Elastic Wave Measurement Using a MEMS AE Sensor

In recent years, with the continuing progress of aging social infrastructures such as bridges and tunnels, there has been high demand for the assessment of deterioration of their performance and conditions. Since current inspection methods for those structures have mainly relied on human resources, it is important to reduce their increasing maintenance cost. One of the key methods for achieving effective maintenance without expensive human costs is to use sensors to discriminate between healthy and unhealthy conditions. In this paper, a MEMS (micro electro mechanical systems) wideband frequency sensor, which is referred to as a super acoustic (SA) sensor, is evaluated through the pencil lead break (PLB) test. Due to its wideband frequency characteristics, the SA sensor is expected to be a promising alternative to the existing vibration sensors, including acoustic emission (AE) sensors. Several PLB signals were generated on an aluminum plate (5 mm thick), and propagating Lamb waves were detected by both AE and SA sensors. SA sensors were able to identify the location of PLB sources on the plate by measuring time differences between each sensor. By comparing the wave spectrums of both the AE and SA sensors analyzed by wavelet transform, the applicability of SA sensor for AE measurement is verified.

Multimodal structural health monitoring based on active and passive sensing

We present a structural health monitoring system based on the simultaneous use of passive and active sensing. The passive approach is based on acoustic emission, whereas the active approach uses the electromechanical impedance and the guided ultrasonic wave methods. As all these methods can be deployed with the use of wafer-type piezoelectric transducers bonded or embedded to the structure of interest, this article describes a unified structural health monitoring system where acoustic emission, electromechanical impedance, and guided ultrasonic wave are integrated in the same hardware/software unit. We assess the feasibility of this multimodal monitoring in a large flat aluminum plate instrumented with six transducers. Acoustic emission events are simulated by exciting a tone burst or by using the conventional pencil lead break test, and the detected signals are processed with a source localization algorithm to identify the position of the source. For the active sensing, damage is simulated by adding a small mass to the plate: the raw waveforms are processed with a delay-and-sum algorithm to create an image of the plate, whereas the electrical admittance of each transducer is analyzed using the statistical index of the root-mean-square deviation. The results presented in this article show that the proposed system is robust, mitigates the weaknesses of each method considered individually, and can be developed further to address the challenges associated with the structural health monitoring of complex structures.

Acoustic emission method for defect detection and identification in carbon steel welded joints

Detecting welding defects in industrial equipment (welded joints and built-up structures) is a key aspect in evaluating the probability of failure in different situations. Acoustic emission (AE) is an effective non-destructive detecting technique, and can be a promising application for welding defect detection. This work presents a systematic experimental investigation on using AE technique for detecting and classifying different weld defects in carbon steel joint material. Four certified carbon steel samples were used in this study. A defect free control sample was used as the reference and three samples with induced defects, namely slag, porosity and crack. A pencil lead break (PLB) test was used to generate simulated AE sources on one side of the joint whereas the AE sensor was mounted on the other side to capture AE signals. A total of four experimental arrangements were used to investigate the effect of propagating distance (sensor to source distance) on the ability of AE to detect and identify defects in welds. For each of these arrangements, AE features such as peak amplitude, rise time, decay time, duration, and count numbers along with statistical features such as AE energy, root mean square (RMS) were extracted and analysed. Also, frequency analysis using FFT and wavelet transform were investigated for each weld test specimen for all arrangements. The results show that AE energy, peak amplitude and RMS value can be used to automatically detect and identify the presence of a defect in carbon steel welds. It is concluded that AE has a considerable potential in use in welding inspection to assess the overall structural health and identify defects that can significantly reduce the strength and reliability of welded material and consequently reduce the risk of component's failure.

Theoretical and Experimental Studies of Localization Methodology for AE and Microseismic Sources Without Pre-Measured Wave Velocity in Mines

The acoustic emission (AE) and microseismic (MS) monitoring are efficient methods to detect faults/breaking signals for both healthy evaluation and disaster control in mining engineering. This paper presents an MS or AE source location method without the need for a pre-measured wave velocity. It can eliminate the location errors for MS/AE monitoring systems caused by the deviations of the wave velocity. To verify the applicability of the proposed method, first, tests of both the pencil lead break and the thermal fracture in granite were carried out, and location errors were compared and analyzed. Results show that the location accuracy of the proposed method is significantly improved, which is superior to the results of the traditional location method (TM) using pre-measured wave velocity. Second, blasting experiments were carried out in Dongguashan copper mine in China. The blasts were used as simulated seismic sources. Average values of absolute distance errors of the MS/AE source locations resulting from the proposed method without wave velocity and the traditional method using average measured wave velocity are 10.16 and 17.55 m, respectively. It shows that the calculated locations by the proposed method are in better agreement with the real blast coordinates. Third, the proposed method is also applied to previously published data. It gives superior results compared with the considered existing methods. Results of the pencil lead break tests, the thermal fracture experiment in granite, and the blasting experiments (including published data) have demonstrated that the proposed method can not only decrease the location errors induced by measurement deviation of velocity, but also locate the MS/AE source in real time, which is a beneficial complement to the method TM in mines.

Micromachined lead zirconium titanate thin-film-cantilever-based acoustic emission sensor with poly(N-isopropylacrylamide) actuator for increasing contact pressure

This paper presents an innovative acousticemission (AE) sensor with a cantilever sensing structure. A hydrothermal lead zirconium titanate (PZT) film was deposited on the cantilever for AE sensing, and an SU8 micropillar at the free end of the cantilever served as an AE wave coupler; in addition, a poly(N-isopropylacrylamide)-based thermoresponsive actuator was integrated with the cantilever to increase the contact pressure exerted on the target. The AE sensor showed higher performance compared with an existing commercial AE sensor. Micromachining technology was used to fabricate AE sensors, and an array of four sensors was fabricated on a 50 μm thick titanium substrate of dimensions 15 mm × 15 mm. The piezoelectric properties of the hydrothermal PZT film were verified by electrically driving the cantilever and measuring the displacement; the piezoelectric constant d31 of the cantilever was 2.43 pC N−1. The output force of the sensing cantilever generated by activating the thermoresponsive actuator was determined. For an electrical power input of 2.5 W, the maximum force output at the SU8 micropillar was 1 N. This force corresponded to the application of a pressure of 1.4 MPa on the target. Pencil lead break tests were conducted to determine and compare the performance of the proposed AE sensor with commercial sensors. Here, experimental and theoretical discussions on the effect of the activation of the thermoresponsive actuator of the proposed AE sensor on AE detection are presented.

Normalization and source separation of acoustic emission signals for condition monitoring and fault detection of multi-cylinder diesel engines

A signal processing technique is presented in this paper to normalize and separate the source of non-linear acoustic emission (AE) signals of a multi-cylinder diesel engine for condition monitoring applications and fault detection. The normalization technique presented in the paper overcomes the long-existing non-linearity problem of AE sensors so that responses measured by different AE sensors can be quantitatively analysed and compared. A source separation algorithm is also developed in the paper to separate the mixture of the normalized AE signals produced by a multi-cylinder diesel engine by utilising the system parameters (i.e., wave attenuation constant and the arrival time delay) of AE wave propagation determined by a standard pencil lead break test on the engine cylinder head. It is shown that the source separation algorithm is able to separate the signal interference of adjacent cylinders from the monitored cylinder once the wave attenuation constant and the arrival time delay along the propagation path are known. The algorithm is particularly useful in the application of AE technique for condition monitoring of small-size diesel engines where signal interference from the neighbouring cylinders is strong.

Localization of acoustic emission sources in structural health monitoring of masonry bridge

Summary The application of the acoustic emission (AE) technique to monitoring the crack characterization in masonry structures is investigated in this paper. An improved approach, modified from the classical crack source localization method, is proposed to provide more reliable crack locations in masonry structures. The introduced parameter, degree of inhomogeneity ξ in the modified method, can help to decrease the unavoidable propagation delay related to refraction due to the layers in masonry material. The modified method is successfully applied to the AE source detection during the pencil-lead break test and the central pier settlement test of a masonry model bridge to analyze the crack pattern. It is shown that the proposed method is a reliable tool for crack source location in masonry structures. Copyright © 2014 John Wiley & Sons, Ltd.