ACOUSTIC AND ELECTROMAGNETIC EMISSION FROM CRACK CREATED IN ROCK SAMPLE UNDER DEFORMATION

Similar documents
Experimental Study on Feature Selection Using Artificial AE Sources

Spectrum and Energy Distribution Characteristic of Electromagnetic Emission Signals during Fracture of Coal

DETECTION AND SIZING OF SHORT FATIGUE CRACKS EMANATING FROM RIVET HOLES O. Kwon 1 and J.C. Kim 1 1 Inha University, Inchon, Korea

MEASUREMENT OF SURFACE DISPLACEMENT EXCITED BY EMAT TRANSDUCER

NONLINEAR C-SCAN ACOUSTIC MICROSCOPE AND ITS APPLICATION TO CHARACTERIZATION OF DIFFUSION- BONDED INTERFACES OF DIFFERENT METALS

Recommendation of RILEM TC 212-ACD: acoustic emission and related NDE techniques for crack detection and damage evaluation in concrete*

EXPERIMENTAL TRANSFER FUNCTIONS OF PRACTICAL ACOUSTIC EMISSION SENSORS

Acquisition and Analysis of Continuous Acoustic Emission Waveform for Classification of Damage Sources in Ceramic Fiber Mat

ARRIVAL TIME DETECTION IN THIN MULTILAYER PLATES ON THE BASIS OF AKAIKE INFORMATION CRITERION

Electronic Noise Effects on Fundamental Lamb-Mode Acoustic Emission Signal Arrival Times Determined Using Wavelet Transform Results

Experimental Study on Quantitative Application of Electromagnetic Radiation Excited by Coal-rock Fracture

Acoustic Emission Basic Process and Definition

HIGH-ORDER STATISTICS APPROACH: AUTOMATIC DETERMINATION OF SIGN AND ARRIVAL TIME OF ACOUSTIC EMISSION SIGNALS

EFFECTS OF LATERAL PLATE DIMENSIONS ON ACOUSTIC EMISSION SIGNALS FROM DIPOLE SOURCES. M. A. HAMSTAD*, A. O'GALLAGHER and J. GARY

Preliminary study of the vibration displacement measurement by using strain gauge

DEVELOPMENT OF HEAT-RESISTANT OPTICAL FIBER AE SENSOR

Research Center for Advanced Science and Technology The University of Tokyo Tokyo 153, Japan

Method of Determining Effect of Heat on Mortar by Using Aerial Ultrasonic Waves with Finite Amplitude

CHAPTER 3 ACOUSTIC EMISSION TECHNIQUE FOR DETECTION AND LOCATION OF PD

MATERIALS CHARACTERIZATION USING LASER ULTRASONIC GUIDED WAVES

System Inputs, Physical Modeling, and Time & Frequency Domains

FATIGUE CRACK CHARACTERIZATION IN CONDUCTING SHEETS BY NON

BENDING FRACTURE BEHAVIOR OF 3D-WOVEN SiC/SiC COMPOSITES WITH TRANSPIRATION COOLING STRUCTURE CHARACTERIZED BY AE WAVELET ANALYSIS

PRACTICAL ASPECTS OF ACOUSTIC EMISSION SOURCE LOCATION BY A WAVELET TRANSFORM

ANALYSIS OF ACOUSTIC EMISSION FROM IMPACT AND FRACTURE OF CFRP LAMINATES

Acoustic Emission For Damage Monitoring of Glass /Polyester Composites under Buckling Loading

ULTRASONIC SPECTROSCOPY OF SILICON SINGLE CRYSTAL

Chapter 5. Signal Analysis. 5.1 Denoising fiber optic sensor signal

Borehole vibration response to hydraulic fracture pressure

DAMAGE IN CARBON FIBRE COMPOSITES: THE DISCRIMINATION OF ACOUSTIC EMISSION SIGNALS USING FREQUENCY

Tool Condition Monitoring using Acoustic Emission and Vibration Signature in Turning

MONITORING THE EVOLUTION OF INDIVIDUAL AE SOURCES IN CYCLICALLY LOADED FRP COMPOSITES

Polytechnic University, Jiaozuo, Henan, China, China, University, Montreal, QC, H3G 1M8, Canada

Absolute Calibration of Acoustic Emission Transducers as per CEN ISO/TR in Disuse of Mechanical Sound Sources or Reference Transducers

Transmission Line Transient Overvoltages (Travelling Waves on Power Systems)

FATIGUE CRACK GROWTH MONITORING OF AN ALUMINUM JOINT STRUCTURE

ACOUSTIC EMISSION OF COMPOSITE WING SEGMENT DURING FATIGUE TESTS

EWGAE 2010 Vienna, 8th to 10th September

Excitation and reception of pure shear horizontal waves by

ULTRASONIC GUIDED WAVE ANNULAR ARRAY TRANSDUCERS FOR STRUCTURAL HEALTH MONITORING

About the High-Frequency Interferences produced in Systems including PWM and AC Motors

Transducer degradation and high amplitude behavior of broadband piezoelectric stack transducer for vibrothermography

Mathematical Model and Numerical Analysis of AE Wave Generated by Partial Discharges

ID-1223 Determination of delamination onset in composite laminates by application of acoustic emission INTRODUCTION

*Department of Physics, ** Department of Building Structures, Faculty of Civil Engineering, Brno University of Technology

LAMB-WAVE ACOUSTIC EMISSION FOR CONDITION MONITORING OF TANK BOTTOM PLATES

Rayleigh Wave Interaction and Mode Conversion in a Delamination

Module 1: Introduction to Experimental Techniques Lecture 2: Sources of error. The Lecture Contains: Sources of Error in Measurement

DAMAGE EVALUATION BY FREQUENCY ANALYSIS OF CONTINU- OUS RECORDED AE WAVEFORM

A NEW MOTION COMPENSATION TECHNIQUE FOR INFRARED STRESS MEASUREMENT USING DIGITAL IMAGE CORRELATION

Use of parabolic reflector to amplify in-air signals generated during impact-echo testing

Radiated EMI Recognition and Identification from PCB Configuration Using Neural Network

ULTRASOUND IN CFRP DETECTED BY ADVANCED OPTICAL FIBER SENSOR FOR COMPOSITE STRUCTURAL HEALTH MONITORING

430. The Research System for Vibration Analysis in Domestic Installation Pipes

Keywords: Ultrasonic Testing (UT), Air-coupled, Contact-free, Bond, Weld, Composites

DEVELOPMENT OF MEASUREMENT SYSTEM USING OPTICAL FIBER AE SENSORS FOR ACTUAL PIPING

Quantitative Crack Depth Study in Homogeneous Plates Using Simulated Lamb Waves.

Evaluation of Partial Discharge in Power Transformers by Acoustic Emission Method and Propagation Modeling of Acoustic Signal

Piezoelectric Fiber Composite Ultrasonic Transducers for Guided Wave Structural Health Monitoring

PIEZOELECTRIC TRANSFORMER FOR INTEGRATED MOSFET AND IGBT GATE DRIVER

Experimental study of slider dynamics induced by contacts with disk asperities

NEURAL NETWORK FATIGUE LIFE PREDICTION IN NOTCHED BRIDGE STEEL I-BEAMS FROM ACOUSTIC EMISSION AMPLITUDE DATA

THE EXTRACTION METHOD FOR DISPERSION CURVES FROM SPECTROGRAMS USING HOUGH TRANSFORM

Module 2 WAVE PROPAGATION (Lectures 7 to 9)

Ultrasonic Transmission Characteristics of Continuous Casting Slab for Medium Carbon Steel

Acoustic emission signal attenuation in the waveguides used in underwater AE testing.

A minimum hydrophone bandwidth for undistorted cavitation noise measurement

Measurement of phase velocity dispersion curves and group velocities in a plate using leaky Lamb waves

NEW APPROACH TO ACOUSTIC EMISSION TESTING METALLIC PRESSURE VESSELS

A Principal Component Analysis of Acoustic Emission Signals from a Landing Gear Component

Introduction to Measurement Systems

DESIGN, CONSTRUCTION, AND THE TESTING OF AN ELECTRIC MONOCHORD WITH A TWO-DIMENSIONAL MAGNETIC PICKUP. Michael Dickerson

EMC TEST REPORT For MPP SOLAR INC Inverter/ Charger Model Number : PIP 4048HS

DEVELOPMENT OF STABILIZED AND HIGH SENSITIVE OPTICAL FI- BER ACOUSTIC EMISSION SYSTEM AND ITS APPLICATION

JOURNAL OF ACOUSTIC EMISSION

PARTIAL DISCHARGE MEASUREMENT

Ultrasonic Guided Wave Testing of Cylindrical Bars

Dynamic Strain Measurement Using Improved Bonding Fiber Bragg Grating

Ultrasonic Imaging of Tight Crack Surfaces by Backscattered Transverse Wave with a Focused Transducer

Advances in Computational High-Resolution Mechanical Spectroscopy HRMS

Experimental Research on Cavitation Erosion Detection Based on Acoustic Emission Technique

Electronic Instrumentation and Measurements

Analysis of Propagation Paths of Partial Discharge Acoustic Emission Signals

CONTINUOUS DAMAGE MONITORING TECHNIQUES FOR LAMINATED COMPOSITE MATERIALS

Paper Title: FIELD MONITORING OF FATIGUE CRACK ON HIGHWAY STEEL I- GIRDER BRIDGE

GIS Disconnector Switching Operation VFTO Study

Application Note. Airbag Noise Measurements

The study on the woofer speaker characteristics due to design parameters

MEASUREMENT OF STRAIN AND POLARIZATION IN PIEZOELECTRIC AND ELECTROSTRICTIVE ACTUATORS

Alternative Coupling Method for Immunity Testing of Power Grid Protection Equipment

SERIES K: PROTECTION AGAINST INTERFERENCE

ACOUSTIC EMISSION MEASUREMENTS ON SHELL STRUCTURES WITH DIRECTLY ATTACHED PIEZO-CERAMIC

A Study on Correlation of AE Signals from Different AE Sensors in Valve Leakage Rate Detection

Independent Tool Probe with LVDT for Measuring Dimensional Wear of Turning Edge

describe sound as the transmission of energy via longitudinal pressure waves;

DAMAGE-TYPE IDENTIFICATION IN A CFRP CROSS-PLY LAMINATE FROM ACOUSTIC EMISSION SIGNALS DETECTED BY A FIBER-OPTIC SENSOR IN A NEW REMOTE CONFIGURATION

The influence of gouge defects on failure pressure of steel pipes

CHAPTER 5 CONCEPT OF PD SIGNAL AND PRPD PATTERN

being developed. Most up and coming drugs are extremely expensive and limited in

An acousto-electromagnetic sensor for locating land mines

Transcription:

ACOUSTIC AND ELECTROMAGNETIC EMISSION FROM CRACK CREATED IN ROCK SAMPLE UNDER DEFORMATION YASUHIKO MORI 1, YOSHIHIKO OBATA 1 and JOSEF SIKULA 2 1) College of Industrial Technology, Nihon University, Izumi 1-2-1, Narashino, Chiba 275-8575, Japan; 2) Czech Noise Research Laboratory, Brno University of Technology Technicka 8, CZ-616 00 Brno, Czech Republic Abstract In order to characterize the generations of electromagnetic emission (EME) and cracks created inside the materials in detail, we measured EME under monotonously increasing and repeated compressive loading of the granite sample. In the loading tests, AE signals were simultaneously measured with the EME signals, so that the generation of EME could be directly compared with the entire fracture process of the rock sample estimated by AE. In the uniaxial compressive tests, the sample was loaded at different displacement speeds from 0.02 to 0.5 mm/min. In comparison with AE, the number of EME events discriminated is lower, because of a lower signal-to-noise ratio of EME channel. The relationship between signal amplitudes of EME and AE suggests the existence of the correlation between AE and EME. The EME signals with the larger amplitude are associated with the AE signals of larger amplitude. Dispersion observed in the EME vs. AE signal amplitude plots is related to crack orientation with respect to EME electrodes. This paper also demonstrates that the simultaneous measurement of AE and EME would be useful for estimating the rock in-situ stress, as an example. Keywords: NDT, Electromagnetic emission (EME), Cracks, Granite Introduction It has been known that the changes in geo-electric potential and the anomalous radiation of geo-electromagnetic waves were observed before major earthquakes. These phenomena also have been observed in laboratory experiments on rock samples, and it was found that micro- and macro-cracking processes are often accompanied by acoustic and electromagnetic emission [1-7]. Generation of cracks in solids is accompanied by the generation of electric charges and the mechanical vibrations. Mechanical vibrations generate acoustic emission (AE) signal. The crack surfaces are electrically charged due to the loss of chemical bonds. Electric charges at the faces of the newly created cracks constitute an electric dipole or quadrupole system. They are sources of electromagnetic field and measurable quantity of this phenomenon is called electromagnetic emission (EME). EME signal can be detected by capacitive electrodes placed on the sample surfaces of low electrical conductivity. In this case the electric field vs. time is detected. A coil can detect the magnetic field of good electrical conductors. Experimentally we have observed two different type of EME signals like damped harmonic motion with: (i) exponentially time-dependent transient value (Fig. 1(a)) and (ii) constant average value (Fig. 1(b)). We suppose that the first one is generated by electrical dipole structure and second one is generated by an electrical quadrupole [8]. J. Acoustic Emission, 27 (2009) 157 2009 Acoustic Emission Group

(a) Signal generated by dipole. (b) Signal generated by quadrupole. Fig. 1 Two type of EME signal waveforms seen in the compression test of granite sample [8]. We also found that the voltage on the measuring capacitor is directly proportional to the electric charge distribution. The recorded electric signal is superposition of crack walls self -vibration given by the crack geometry and vibration due to an ultrasonic wave given by sample dimensions. The EME signal precedes the AE response and the time delay corresponds to the distance between AE sensor and crack position due to the difference of propagation velocities of sound and electromagnetic field in the sample. In order to characterize the generations of EME and cracks created inside the materials in detail, the measurements of EME were conducted under the monotonously increasing compressive loading test and the repeated compressive loading test of the granite sample. In the loading tests, AE signals were simultaneously measured with the EME signals, so that the generation of EME could be directly compared with the entire fracture process of the rock sample estimated by AE. This paper also demonstrates that the simultaneous measurement of AE and EME would be useful for estimating the rock in-situ stress, as an example. EME under Uniaxial Compression A rectangular block sample of Inada Granite with dimensions of 10 mm x 10 mm square and height of 30 mm was deformed under the uniaxial compression stress. Schematic sample assembly is shown in Fig. 2. Details of the loading set-up are described in [3]. AE transducer with a frequency range of 200 to 700 khz was mounted on the sample surface, as shown in Fig. 2(b), to detect the AE signals during the loading test. EME signals from the rock sample were detected as the electric potential change appearing between two electrodes A-A. The electrodes were formed by painting a conductive paste on the sample surfaces, as shown in Fig. 2(b). Both the EME and AE signals were amplified by 40-dB preamplifiers and led into the input channels of the AE system used. When an AE signal was detected at AE transducer, the AE and EME signals were digitized and stored on the memory in the AE system. The threshold level of 60 db was used for AE event detection. To eliminate interference arising from ambient noise, the sample and the preamplifiers were electromagnetically shielded by using a thick aluminum alloy chamber. In the uniaxial tests, the sample was loaded at different displacement speeds ranged from 0.02 to 0.5 mm/min, so that the effects of displacement speed on the generation behaviors of EME and AE were examined. 158

Fig. 2 Sample assembly: (a) setup for uniaxial loading and (b) arrangement of AE transducer, electrodes for EME detection, and strain gages. Fig. 3 A set of simultaneous measurement of EME and AE signals recorded in the compression test conducted on Inada Granite. The background noise of EME signal channel is larger than that of the AE channel, since the electrodes for EME detection forms a high-impedance circuit with more than 10-time higher equivalent noise resistance than that of corresponding AE transducer. This result in difficulty of discriminating the small amplitude EME signals expected. In Fig. 3, three strong AE signals are clearly recognized. As the onset of AE signal indicated by an arrow on the EME waveform matches the EME signal onset, it can be concluded that this EME signal is associated with AE. In this manner, even small amplitude EME signals, such as the signal generated near 100 µs in Fig. 3, are detectable. In the present experiments, most of EME signals recorded were classified into the type of signal generated by electrical quadrupole structure, of which waveform was shown in Fig. 1(b). 159

160 (b) displacement speed = 0.06 mm/min. (c) displacement speed = 0.5 mm/min. Fig. 4 Results of simultaneous measurements of AE and EME signals during monotonously increasing compression tests of the granite samples conducted at three different displacement speeds of 0.02, 0.06 and 0.5 mm/min. (a) displacement speed = 0.02 mm/min.

We have observed that the EME signal is generated several µs before AE generation. This delay in AE is expected as EME signal propagates with the velocity of electromagnetic waves while AE signal propagates with the velocity of mechanical waves. Origin of EME signal corresponds to the time of crack creation. This effect was used to improve crack localization [3]. The results of simultaneous measurements of AE and EME signals during monotonously increasing compression tests of the granite samples conducted at three different displacement speeds of 0.02, 0.06 and 0.5 mm/min. are shown in Figs. 4(a), (b) and (c), respectively. In Fig. 4, applied load (P) and dilatant strain of the rock sample (ε v ), cumulative AE event count (N AE ) and EME event count (N EME ), and amplitude of AE event signal (V AEp ) and EME event signal (V EMEp ) are plotted as a function of elapsed time of the loading (t). Generation of AE starts at a level of dilatant strain of the sample, where the dilatant strain deviates from the linear trend, and increases until the sample failure takes place. Generation of EME events starts after AE generation and increases with an increase of the applied stress. It is also observed that the generation of active EME events is peculiar to the stressing stage, at which the volume of sample changes from the contraction due to compression to the dilatancy developed in a direction vertical to the sample axis stressed. This result suggests that the EME signals were emitted from the micro-crack created in the tensile direction normal to the applied load. In comparison to AE, the number of EME events discriminated is lower, because of a lower signal-to-noise ratio of EME channel. It can be said that the displacement speed has no effect on the generation behavior of AE and EME during the stressing. However, there is a difference in the number of total AE event counts among three displacement speeds. Though the total event counts measured in the tests conducted at the displacement speeds of 0.02 and 0.06 mm/min show almost the same value of 20,000, while only one fifth of the value, i.e., 4000 counts were measured in the test at the displacement speed of 0.5 mm/min. The reason for the difference in event counts observed are explained as follows: The waveforms shown in Fig. 3 were the simultaneously recorded AE and EME signal waveforms detected in the test conducted at a displacement speed of 0.5 mm/min. By the visual observation three strong AE event signals are recognized in a period of 500 µs. In this case, a dead time for AE detection was set at 1 ms on the AE system used. Therefore, the AE system was triggered by the first hit signal generated at about 100 µs, and counted the signals as one event, even though three events were visually recognized. In general, it is expected that when the material is stressed, the time interval of micro-crack creation in the material becomes short with increasing deformation rate of the stressing. Thus, when the frequency of the occurrence of micro-cracks exceeds the AE dead time, as seen in Fig. 3, the AE event counts measured by the AE system show a smaller value than actual. Next, the plots of the signal amplitude of EME (V EMEp ) and AE (V AEp ) as a function of the elapsed time of loading, are shown in Fig. 4. These clearly demonstrate that the EME and AE events having the larger signal amplitude are generated with increasing time: i.e., with increase of the dilatant strain of the sample. This nature is irrespective to the displacement speeds tested. 161

Fig. 5 Relationship between EME (V EMEp ) and AE (V AEp ) signal amplitude. The EME signals detected in the loading tests, without exception, were accompanied with the generation of AE signals. Thus, the relationship between the signal amplitudes of EME and AE was analyzed for the experimental results shown in Fig. 4. The results, which are summarized in Fig. 5, strongly suggest the existence of the positive correlation between AE and EME in the signal amplitude. That is, the EME signals with the larger amplitude associate with the AE signals of larger amplitude. The signal amplitude of EME would correspond to the amount of the electric charges re-distributed on the newly created crack surfaces. It is expected that the amount of electric charges would depend on the size of the crack surfaces created. Dispersion observed on the relationship in Fig. 5 is related to crack orientation with respect to EME electrodes. 162

EME under Repeated Loading Rectangular block samples of Inada Granite, of which dimensions were 20 mm x 20 mm x 80 mm and 10 mm x 10 mm x 40 mm, were stressed in repeated uniaxial compression load. The sample was pre-loaded up to a certain value and then unloaded. It is then loaded to a larger value and then unloaded again. This loading-unloading procedure was repeated until the sample rupture took place. Each of repeated loading-unloading procedures was conducted at a constant displacement speed of 0.5 mm/min. The sample assembly for the repeated loading test was basically the same as that shown in Fig. 2. In the repeated loading test, the EME signals were amplified by 20 db using 3S Sedlak PA 21 ultra-low-nose preamplifier. The internal noise level of PA21 preamplifier was 3 nv/ Hz in the frequency range of 500 Hz 10 MHz. The output signals of PA21 were further amplified by 40 db with a PAC 1220A preamplifier. AE signals were amplified by 40 db with a PAC 1220A preamplifier. In addition, the sample assembly and the preamplifiers were electromagnetically shielded. The threshold levels were 70 db for EME signal and 60 db for AE signal. The result of simultaneous measurements of EME and AE signals during the repeated loading test conducted on the block sample of 20 mm x 20 mm x 80 mm is shown in Fig. 6. The loading history for the repeated loading test is shown as the stress curve, σ. After an initial stress of 12.5 MPa, which corresponds to approximately 10 % of compression strength of the rock tested, was applied, the maximum stress level of successive loading was increased by 12.5 MPa steps until the sample failure. In Fig. 6, EME and AE events are represented by the cumulative event counts measured for each stressing step. Fig. 6 History of the repeated stressing test and the result of simultaneous measurement of EME and AE signals conducted on Inada Granite sample of 20 mm x 20 mm x 80 mm. 163

Fig. 7 Expanded stressing steps #3 and #8 in the test procedure shown in Fig. 6. The EME signals are detected during every stressing step, with the exception of step #1. The EME signals are generated only in the stage where the applied stress increases and reaches at the maximum stress level for each step, whereas the generation of AE signals is observed not only in the stress increasing stage but also in the unloading stage. For example, the details of the stressing steps #3 and #8 are shown in Fig. 7. In the figures, the arrowhead indicates the stress corresponding to the maximum pre-stress for each step. In Fig. 7, open symbol plotted on the stress curve denotes the onset stress of active AE event generation during the stress increasing stage. Similarly, the solid symbol on the stress curve denotes the onset stress of EME signal. These onset stresses of AE and EME are referred as σ AE and σ EME, respectively. Fig. 8 Plots of estimated stresses σ EME and σ AE as a function of the pre-stress σ pre. The stresses plotted are normalized by the strength of the rock sample tested σ b. The values of the σ AE and σ EME for each repeated stressing step as shown in Fig. 6 and also for the experimental results obtained in the test conducted on the block sample of 10 mm x 10 mm x 40 mm were evaluated. In Fig. 8, the σ AE and σ EME obtained are plotted as a function of the pre-stress level σ pre. In the figure, these stresses are normalized by the compression strength of the rock tested, σ b. The Kaiser effect of AE is well recognized in the range of the pre-stress below 22 % of the σ b, giving the underestimation of pre-stress. Specifically, the amount of the underestimation is 10 % to 25 % of the actual pre-stress. This underestimation would be caused by 164

the fact that the AE due to the friction between the fracture surfaces induced during the previous loading process is measured in the early stage of each successive stressing step. However, it should be noted that in general, Kaiser effect shall be discussed within the region of elastic or elasto-plastic deformation. In the case of the sample tested, it is expected that the corresponding stress range is 30 % of the maximum strength. On the other hand, the onset stress level of EME signal, σ EME, estimates the pre-stress level within the deviation of 10 %, over a wide range of the pre-stress up to 90 % of the compression strength of the sample tested. This result clearly suggests that the emission of EME signals is associated with the creation and/or extension of micro-cracks, and is non-reversible phenomena, similar to the Kaiser effect of AE. Therefore, it can be concluded that the EME signal measurement can estimate more accurately the current stress level, to which the rock samples have been subjected, than that of AE activity. Summary Simultaneous measurement of EME and AE during the monotonously increasing compressive loading test and the repeated loading test has been conducted on granite samples to characterize the EME generation in detail. 1. EME signal is accompanied with the generation of AE signal. In comparison with AE, the number of EME events discriminated is lower, because of a lower signal-to-noise ratio of EME channel. 2. EME and AE signals are correlated and their amplitudes increase just before the sample rupture. 3. The relationship between signal amplitudes of EME and AE suggests the existence of the correlation between AE and EME. The EME signals with the larger amplitude are associated with the AE signals of larger amplitude. Dispersion is related to crack orientation with respect to EME electrodes. 4. The emission of EME signals is associated with the creation and/or extension of micro-cracks, and is non-reversible phenomena, similar to the Kaiser effect of AE. 5. EME signal is detected several µs before AE generation. This delay in AE is expected as EME signal propagates with the velocity of electromagnetic waves while AE signal propagates with the velocity of mechanical waves. Thus, the origin of EME signal corresponds to the time of crack creation, and this effect can be used to improve crack localization by AE. 6. EME signal measurement is applicable to estimate the current stress level, to which the rock samples have been subjected. The greatest advantage is that the current stress can be directly estimated without any post signal analysis. In addition, the detection of EME from rock sample under stressing is very simple and easy, and a usual AE system can be used to record and analyze the EME signals. Acknowledgement This work was partially supported by the Grant Agency of the Czech Republic under Grants 106/07/1393 and project VZ MSM 0021630503. 165

References 1) J. Šikula, B. Koktavy, P. Vašina and T. Lokajíček, Detection of Crack Position by AE and EME Effects in Solids, Proc. of Acoustic Emission Conf., Boulder, CO, USA, 1997. 2) T. Ogawa, K. Oike and T. Miura: Electromagnetic radiation from rocks, J. Geophys. Res., 90, 6245-6249, 1985. 3) P. Sedlak, J. Sikula, T. Lokajicek, Y. Mori, Acoustic and electromagnetic emission as a tool for crack localization, Meas. Sci. Technol. 19, 2008. doi:10.1088/0957-0233/19/4/045701. 4) I. Yamada, K. Masuda, H. Mizutani: Electromagnetic and acoustic emission associated with rock fracture, Phys. Earth. Planet Int., 57, 157-168, 1989. 5) T. Lokajíček and J. Šikula, Acoustic emission and electromagnetic effects in rocks, Progress in Acoustic Emission VIII, 1996, JSNDI, pp. 311-314. 6) Y. Mori, K. Sato, Y. Obata and K. Mogi, Acoustic emission and electric potential changes of rock samples under cyclic loading, Progress in Acoustic Emission IX, 1998, AEWG, II-1. 7) V. Hadjicontis and C. Mavromatou, Transient electric signals prior to rock failure under uniaxial compression, Geophys. Res. Lett., 21, 1994, 1687-1690. 8) Josef Sikula, Yasuhiko Mori, Tomas Lokajicek, Pavel Koktavy, Jiri Majzner and Petr Sedlak, Crack creation kinetics characterization by electromagnetic and acoustic emission, Proc. 28th European Conf. AE Testing, Krakow, Poland, September 2008, 118-123. 166