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Acoustic emission approach to determining survivability in fatigue tests
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect
Procediaonline Computer 00 (2019) 000–000 Available at Science www.sciencedirect.com Procedia Computer Science 00 (2019) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 149 (2019) 282–287
ICTE in Transportation and Logistics 2018 (ICTE 2018) ICTE in Transportation and Logistics 2018 (ICTE 2018)
Acoustic emission approach to determining survivability in fatigue Acoustic emission approach to determining survivability in fatigue tests tests Dmitriy Evseevaa, Boris Medvedeva,a,*, Philip Medvedevaa, Guntis Strautmanisbb, Sergei c Dmitriy Evseev , Boris Medvedev *,Samoshkin Philip Medvedev , Guntis Strautmanis , Sergei Samoshkinc a a
Russian University of Transport (RUT MIIT), 9b9 Obrazcova Street, Moscow 127994, Russia b Riga Technical University Kalku Street, Riga LV-1658, Latvia Russian University of Transport (RUT(RTU), MIIT),19b9 Obrazcova Street, Moscow 127994, Russia c TverbRiga Institute of Railway Car Building, Petersburg sh., LV-1658, Tver 170003, Russia Technical University (RTU), 145g Kalku Street, Riga Latvia c Tver Institute of Railway Car Building, 45g Petersburg sh., Tver 170003, Russia
Abstract Abstract In order to correctly determine of the survivability of rolling stock parts during fatigue tests, it is necessary to accurately In order tothecorrectly determine of thestart. survivability during fatiguemethod tests, of it detecting is necessary to accurately determine time of the fatigue crack This workofis rolling devotedstock to theparts acoustic-emission a fatigue crack at determine the time fatigue crack start.time Thisand work is devoted theapplied. acoustic-emission method detecting a fatigue crack of at the initial stage of of its the growth. Frequency, median filterstoare The technique was of applied during the testing the of its growth. time and median filters are applied. The technique was applied during the testing of sideinitial framesstage of wheeled trolleys Frequency, of freight cars. side frames of wheeled trolleys of freight cars. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by B.V. © 2019 The Authors. by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of the the ICTE in in Transportation Transportation and and Logistics Logistics 2018 2018 (ICTE2018). (ICTE2018). Peer review under responsibility of the scientific committee of ICTE Peer review under responsibility of the scientific committee of the ICTE in Transportation and Logistics 2018 (ICTE2018). Keywords: Survivability; Fatigue; Acoustic emission; Signal processing Keywords: Survivability; Fatigue; Acoustic emission; Signal processing
1. Introduction 1. Introduction When conducting fatigue tests of materials, parts, and structures, along with the determination of fatigue life When conducting teststo ofdetermine materials,additional parts, andparameters, structures, including along withsurvivability. the determination of fatigue parameters, attempts fatigue are made This allows youlife to parameters, attempts arecontent made of to the determine parameters, includingof survivability. This and allows you to increase the information tests andadditional thereby increase the efficiency the test equipment personnel. increase information content of the and thereby increase the test equipment andafter personnel. In thisthe case, we call survivability as tests a quantitative estimate of the efficiency test object of ability to resist the load fatigue In thisoccurs. case, we quantitative estimatethe of the test object ability to resist the load after fatigue damage Thecall keysurvivability point here isastoaaccurately determine beginning of crack growth. damage occurs. The key point here is to accurately determine the beginning of crack growth.
* Corresponding author. Tel.: +7-910-450-4686; fax: +7-910-450-4686. E-mail address:author. [email protected] * Corresponding Tel.: +7-910-450-4686; fax: +7-910-450-4686. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. Peer is review under responsibility ofthe theCC scientific committee the ICTE in Transportation and Logistics 2018 (ICTE2018). This an open access article under BY-NC-ND licenseof(http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of the ICTE in Transportation and Logistics 2018 (ICTE2018).
1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of the ICTE in Transportation and Logistics 2018 (ICTE2018). 10.1016/j.procs.2019.01.136
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
283
Detection of fatigue cracks at the initial stage of its development in a visual way is a difficult task, especially for large objects, complex shapes and not known in advance by the place of occurrence of cracks. Inspections should be carried out regularly, qualified personnel are required and, if the tests last for weeks, the costs of such inspections become a significant increase in the cost of testing. The visual method is inaccurate, not only because of the discrete nature of the examinations, but also because of the influence of the human factor. In box-type constructions, a crack can originate on the inner surface and can only be detected after the crack front has exited to the outer surface. The acoustic emission (AE) control method allows detecting a growing defect at any place of the test object and enables the development of an automated continuous monitoring system for the detection of fatigue cracks. The first results of AE monitoring of fatigue cracks were published in 1974 [1]. At that time, the main parameter of AE was the intensity or count rate of pulses. Since then, many works on this topic have been carried out and published (see, for example, [2-4]). However, the practical significance of these works for industry and transport is insignificant so far. The fatigue crack really grows discretely, generating an acoustic impulse at each jump due to the release of the potential energy of elastic deformation. AE monitoring in this application consists in registering these pulses with several sensors and analyzing the information thus obtained. The problem is that the fatigue tests of real objects are accompanied by significant acoustic noise masking the useful signal. That is why most of the publications on AE monitoring of fatigue cracks are based on testing samples with the use of special equipment to reduce noise. The modern level of development of measuring equipment and computational tools allows us to significantly expand the arsenal of methods for extracting a useful signal against the background of interference. This work is devoted to the development of an algorithm for digital processing of AE signals during fatigue tests for the purpose of early detection of fatigue cracks and determination of survivability parameters. As a test object, cast side frames of wheeled carts of freight railcars are considered. This is one of the most loaded elements of the car, the state of which is crucial for the safety of transportation. Determining the parameters of the survivability of such elements makes it possible to reasonably designate a schedule for inspections and repairs of rolling stock of railway transport. 2. Fatigue testing technique The experimental part of the work was carried out on the test equipment of the Tver Institute of Railway Car Building (Russia, Tver). A batch of 10 side frames of wheeled carts of a freight car, made by casting from steel 20GFL (Russia), a close analogue of J13002 (USA), was tested. The tests were carried out according to the standard method on the testing machine TsDM-200 (see Fig. 1). This machine provides a load profile of ܲ, changing in time as: ܲ ൌ ܲ ܲ ሺʹߨ݂ݐሻ,
(1)
where ܲ is the average load, and ܲ is the amplitude of the variable component of the load. The average load ܲ in this case was 368 kN, the amplitude ܲ varied in the range from 255 to 304 kN. The frequency ݂of variable component of the load was of about 9 Hz. No special measures were taken to reduce the noise of the loading device. 3. Registration and analysis of acoustic emission signals In order to ensure the freedom of choice of methods and algorithms for signal processing, the authors abandoned the use of specialized AE monitoring equipment other than sensors and preamplifiers. Signals were recorded in digital form for further processing. Sensors AES150-S manufactured by Bangos (EU) were used. For convenience of mounting on the frame, the sensors were supplied with magnetic rings, providing sufficient downforce. Acoustic contact was provided by prepolishing the sensor installation site and grease used as a contact liquid.
284
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
Fig. 1. The test object on the testing machine.
PAC 1220C (Physical Acoustics Corporation, USA) preamplifiers with a gain of 40 dB were used to amplify the signals and match the communication line. An example of mounting the sensor and the preamplifier on the frame is shown in Fig. 2.
Fig. 2. Placing the sensor and preamplifier on the frame during testing.
According to the results of the calibration of the acoustic antenna performed using the simulator AE, it was decided to limit the number of channels to two. This amount is sufficient to localize the source of signals and to estimate their spectral composition. At the same time, the reduction in the number of channels has significantly reduced the amount of information recorded and the duration of computational procedures during its processing. In Fig. 3 shows a block diagram of the equipment for recording AE signals.
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
285
Fig. 3. The block diagram of the equipment recording signals AE.
An inexpensive ISDS205B dual-channel USB oscilloscope (INSTRUSTAR, China) was used as a digital recorder, which has the function of continuous recording of a digitized signal to 4GB. Analog-to-digital conversion was carried out with a frequency of 2 MHz and a depth of 8 bits on both channels. To extend the dynamic range of the analog-digital path, a programmable attenuator at the input of the ISDS205B recorder was used, which allows changing the range of the recorded signal in a wide range – from ± 50 mV to ± 50 V. The recorder was controlled by a computer via the USB interface using the specialized software Multi VirAnalyzer (INSTRUSTAR). The same program was used for operational monitoring during the testing process. The recorded signal was recorded on the hard disk of a personal computer (PC1) for further processing. The PC1 was equipped with a 4G GSM modem for connecting to the Internet. Remote monitoring and control was carried out using the popular TeamViewer program. Remote access to a PC1 has greatly simplified the organization of tests, especially at the stage of lengthy fatigue tests. To ensure the rapid development of signal processing tools, a software environment with a convenient graphical user interface LabVIEW (National Instruments, USA) was used. The first stage of processing the registered AE signals was the use of a median filter to get rid of small impulse noise of electromagnetic nature, which are sometimes present in the signal. The appearance of such interference is explained by the location of the test benches of the Tver Institute of Railway Car Building on the territory of the Tver Railway Car Building Plant with a large number of welding and other energy-intensive equipment that "pollutes" the electromagnetic environment. Digital filtering was used to isolate the signal from the noise of the test equipment. As a result of a preliminary analysis, it was found that the main noise power is concentrated in the range below 50 kHz. In this case, the noise is also present at high frequencies, but the noise level decreases with increasing frequency. As a result, the frequency band was selected 165-185 kHz. A 4th order Chebyshev bandpass filter was used. An example of a filtering is shown in Fig. 4. In order to get rid of the noise of the loading device, the same filter was used in the Multi VirAnalyzer program during operational monitoring of the testing process. Tests have shown that along with the pulses accompanying the crack jumps, there is a secondary AE associated with the dynamic contacting of the crack faces. This signal, in contrast to the noise of the loading device, can be wide enough to make it difficult to distinguish it from the signals of a growing crack. The solution to this problem was proposed by Danegan and Harris [1]. They noticed that fatigue crack jumps occur only at a load close to the maximum load of the cycle. We called these signals primary. The crack signals, appearing as lesser loads and not directly related to crack growth, we called secondary. To isolate the primary crack signals, it is thus necessary to record the load profile simultaneously with the AE signals. To obtain information about the load profile, data from an ADXL345 (Analog Devices) accelerometer installed on the test object near the point of maximum deflection were recorded simultaneously with the registration of AE signals. Since the accelerometer readings are proportional to the variable component of the load ܲ ሺʹߨ݂ݐሻ (1), they allow to extract fragments of AE signals corresponding to the maximum load values: ܲ ܲǤ଼
(2)
286
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
where ��.� � �� � �.��� . The result is shown in Fig. 5. We considered the onset of crack growth to be the appearance of the first pulses that passed all the described filters with an amplitude of at least 0.1 V, in at least one channel.
Fig. 4. AE signal before (a) and after (b) applying a bandpass filter.
4. Determining the parameters of survivability The simplest indicator of survivability is the dimensionless parameter �: � � �� � ��� ���,
(3)
�� ��� � � �� �⁄� ��� .
(4)
where � is the number of load cycles to failure or loss of bearing capacity of the part, ��� is the number of cycles until a fatigue crack appears, detected visually or by other means. Denote the number of cycles until the fatigue ��� �� , and by the AE method – ��� . The parameters S calculated by these crack appears visually detected by ��� ��� �� numbers in accordance with (3) are denoted by � and � , respectively. To determine the correction factor of parameter survivability we used the expression:
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
287
The results of the tests and data processing are shown in Table 1.
Fig. 5. The result of the selection of the AE signal corresponding to the maximum load. Table 1. The results of the tests.
5 133 000
௩௦ ܰ
5 345 000
ܰ
10 000 000
ܵ ா
0,49
ܵ ௩௦
Correction
1
ா ܰ
0,47
4%
2
315 000
1 585 200
2 165 500
0,85
0,27
69%
3
8 300 000
9 080 300
10 000 000
0,17
0,09
46%
4
4 700 000
6 600 000
10 000 000
0,53
0,34
36%
5
1 630 000
2 025 500
3 561 500
0,54
0,43
20%
6
620 000
1 537 000
1 939 900
0,68
0,21
69%
7
1 280 000
1 380 000
1 763 000
0,27
0,22
21%
8
1 100 000
1 600 000
2 205 500
0,50
0,27
45%
9
3 800 000
4 150 000
8 100 000
0,53
0,49
8%
Frame
It follows from the table that the proposed method of recording and processing AE signals made it possible to clarify the vitality index in the direction of its increase from 4% to more than three times. 5. Conclusion The problem of cracks early detection by the acoustic emission method was solved during bench fatigue testing of rolling stock parts. A method for correctly determining the parameters of survivability during fatigue tests was proposed and tested. References [1] Harris, D.O. and H. L. Dunegan. (1974) “Continuous monitoring of fatigue-crack growth by acoustic-emission techniques.” Experimental Mechanics 14(2): 71–81. [2] Morton, T.M., R. M. Harrington, and J. G. Bjeletich. (1973) “Acoustic emissions of fatigue crack growth.” Engineering Fracture Mechanics 5 (3): 691-697. [3] Lindley, T.C., I. G. Palmer, and C. E. Richards. (1978) “Acoustic emission monitoring of fatigue crack growth.” Materials Science and Engineering 32 (1): 1-15. [4] Robertsa, T.M., and M. Talebzadehb. (2003) “Acoustic emission monitoring of fatigue crack propagation.” Journal of Constructional Steel Research 59 (6): 695-712
ScienceDirect ScienceDirect
Procediaonline Computer 00 (2019) 000–000 Available at Science www.sciencedirect.com Procedia Computer Science 00 (2019) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Computer Science 149 (2019) 282–287
ICTE in Transportation and Logistics 2018 (ICTE 2018) ICTE in Transportation and Logistics 2018 (ICTE 2018)
Acoustic emission approach to determining survivability in fatigue Acoustic emission approach to determining survivability in fatigue tests tests Dmitriy Evseevaa, Boris Medvedeva,a,*, Philip Medvedevaa, Guntis Strautmanisbb, Sergei c Dmitriy Evseev , Boris Medvedev *,Samoshkin Philip Medvedev , Guntis Strautmanis , Sergei Samoshkinc a a
Russian University of Transport (RUT MIIT), 9b9 Obrazcova Street, Moscow 127994, Russia b Riga Technical University Kalku Street, Riga LV-1658, Latvia Russian University of Transport (RUT(RTU), MIIT),19b9 Obrazcova Street, Moscow 127994, Russia c TverbRiga Institute of Railway Car Building, Petersburg sh., LV-1658, Tver 170003, Russia Technical University (RTU), 145g Kalku Street, Riga Latvia c Tver Institute of Railway Car Building, 45g Petersburg sh., Tver 170003, Russia
Abstract Abstract In order to correctly determine of the survivability of rolling stock parts during fatigue tests, it is necessary to accurately In order tothecorrectly determine of thestart. survivability during fatiguemethod tests, of it detecting is necessary to accurately determine time of the fatigue crack This workofis rolling devotedstock to theparts acoustic-emission a fatigue crack at determine the time fatigue crack start.time Thisand work is devoted theapplied. acoustic-emission method detecting a fatigue crack of at the initial stage of of its the growth. Frequency, median filterstoare The technique was of applied during the testing the of its growth. time and median filters are applied. The technique was applied during the testing of sideinitial framesstage of wheeled trolleys Frequency, of freight cars. side frames of wheeled trolleys of freight cars. © 2019 The Authors. Published by Elsevier B.V. © 2019 The Authors. Published by B.V. © 2019 The Authors. by Elsevier Elsevier B.V. This is an open accessPublished article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of the the ICTE in in Transportation Transportation and and Logistics Logistics 2018 2018 (ICTE2018). (ICTE2018). Peer review under responsibility of the scientific committee of ICTE Peer review under responsibility of the scientific committee of the ICTE in Transportation and Logistics 2018 (ICTE2018). Keywords: Survivability; Fatigue; Acoustic emission; Signal processing Keywords: Survivability; Fatigue; Acoustic emission; Signal processing
1. Introduction 1. Introduction When conducting fatigue tests of materials, parts, and structures, along with the determination of fatigue life When conducting teststo ofdetermine materials,additional parts, andparameters, structures, including along withsurvivability. the determination of fatigue parameters, attempts fatigue are made This allows youlife to parameters, attempts arecontent made of to the determine parameters, includingof survivability. This and allows you to increase the information tests andadditional thereby increase the efficiency the test equipment personnel. increase information content of the and thereby increase the test equipment andafter personnel. In thisthe case, we call survivability as tests a quantitative estimate of the efficiency test object of ability to resist the load fatigue In thisoccurs. case, we quantitative estimatethe of the test object ability to resist the load after fatigue damage Thecall keysurvivability point here isastoaaccurately determine beginning of crack growth. damage occurs. The key point here is to accurately determine the beginning of crack growth.
* Corresponding author. Tel.: +7-910-450-4686; fax: +7-910-450-4686. E-mail address:author. [email protected] * Corresponding Tel.: +7-910-450-4686; fax: +7-910-450-4686. E-mail address: [email protected] 1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2019 Thearticle Authors. Published by Elsevier B.V. Peer is review under responsibility ofthe theCC scientific committee the ICTE in Transportation and Logistics 2018 (ICTE2018). This an open access article under BY-NC-ND licenseof(http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of the ICTE in Transportation and Logistics 2018 (ICTE2018).
1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under responsibility of the scientific committee of the ICTE in Transportation and Logistics 2018 (ICTE2018). 10.1016/j.procs.2019.01.136
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
283
Detection of fatigue cracks at the initial stage of its development in a visual way is a difficult task, especially for large objects, complex shapes and not known in advance by the place of occurrence of cracks. Inspections should be carried out regularly, qualified personnel are required and, if the tests last for weeks, the costs of such inspections become a significant increase in the cost of testing. The visual method is inaccurate, not only because of the discrete nature of the examinations, but also because of the influence of the human factor. In box-type constructions, a crack can originate on the inner surface and can only be detected after the crack front has exited to the outer surface. The acoustic emission (AE) control method allows detecting a growing defect at any place of the test object and enables the development of an automated continuous monitoring system for the detection of fatigue cracks. The first results of AE monitoring of fatigue cracks were published in 1974 [1]. At that time, the main parameter of AE was the intensity or count rate of pulses. Since then, many works on this topic have been carried out and published (see, for example, [2-4]). However, the practical significance of these works for industry and transport is insignificant so far. The fatigue crack really grows discretely, generating an acoustic impulse at each jump due to the release of the potential energy of elastic deformation. AE monitoring in this application consists in registering these pulses with several sensors and analyzing the information thus obtained. The problem is that the fatigue tests of real objects are accompanied by significant acoustic noise masking the useful signal. That is why most of the publications on AE monitoring of fatigue cracks are based on testing samples with the use of special equipment to reduce noise. The modern level of development of measuring equipment and computational tools allows us to significantly expand the arsenal of methods for extracting a useful signal against the background of interference. This work is devoted to the development of an algorithm for digital processing of AE signals during fatigue tests for the purpose of early detection of fatigue cracks and determination of survivability parameters. As a test object, cast side frames of wheeled carts of freight railcars are considered. This is one of the most loaded elements of the car, the state of which is crucial for the safety of transportation. Determining the parameters of the survivability of such elements makes it possible to reasonably designate a schedule for inspections and repairs of rolling stock of railway transport. 2. Fatigue testing technique The experimental part of the work was carried out on the test equipment of the Tver Institute of Railway Car Building (Russia, Tver). A batch of 10 side frames of wheeled carts of a freight car, made by casting from steel 20GFL (Russia), a close analogue of J13002 (USA), was tested. The tests were carried out according to the standard method on the testing machine TsDM-200 (see Fig. 1). This machine provides a load profile of ܲ, changing in time as: ܲ ൌ ܲ ܲ ሺʹߨ݂ݐሻ,
(1)
where ܲ is the average load, and ܲ is the amplitude of the variable component of the load. The average load ܲ in this case was 368 kN, the amplitude ܲ varied in the range from 255 to 304 kN. The frequency ݂of variable component of the load was of about 9 Hz. No special measures were taken to reduce the noise of the loading device. 3. Registration and analysis of acoustic emission signals In order to ensure the freedom of choice of methods and algorithms for signal processing, the authors abandoned the use of specialized AE monitoring equipment other than sensors and preamplifiers. Signals were recorded in digital form for further processing. Sensors AES150-S manufactured by Bangos (EU) were used. For convenience of mounting on the frame, the sensors were supplied with magnetic rings, providing sufficient downforce. Acoustic contact was provided by prepolishing the sensor installation site and grease used as a contact liquid.
284
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
Fig. 1. The test object on the testing machine.
PAC 1220C (Physical Acoustics Corporation, USA) preamplifiers with a gain of 40 dB were used to amplify the signals and match the communication line. An example of mounting the sensor and the preamplifier on the frame is shown in Fig. 2.
Fig. 2. Placing the sensor and preamplifier on the frame during testing.
According to the results of the calibration of the acoustic antenna performed using the simulator AE, it was decided to limit the number of channels to two. This amount is sufficient to localize the source of signals and to estimate their spectral composition. At the same time, the reduction in the number of channels has significantly reduced the amount of information recorded and the duration of computational procedures during its processing. In Fig. 3 shows a block diagram of the equipment for recording AE signals.
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
285
Fig. 3. The block diagram of the equipment recording signals AE.
An inexpensive ISDS205B dual-channel USB oscilloscope (INSTRUSTAR, China) was used as a digital recorder, which has the function of continuous recording of a digitized signal to 4GB. Analog-to-digital conversion was carried out with a frequency of 2 MHz and a depth of 8 bits on both channels. To extend the dynamic range of the analog-digital path, a programmable attenuator at the input of the ISDS205B recorder was used, which allows changing the range of the recorded signal in a wide range – from ± 50 mV to ± 50 V. The recorder was controlled by a computer via the USB interface using the specialized software Multi VirAnalyzer (INSTRUSTAR). The same program was used for operational monitoring during the testing process. The recorded signal was recorded on the hard disk of a personal computer (PC1) for further processing. The PC1 was equipped with a 4G GSM modem for connecting to the Internet. Remote monitoring and control was carried out using the popular TeamViewer program. Remote access to a PC1 has greatly simplified the organization of tests, especially at the stage of lengthy fatigue tests. To ensure the rapid development of signal processing tools, a software environment with a convenient graphical user interface LabVIEW (National Instruments, USA) was used. The first stage of processing the registered AE signals was the use of a median filter to get rid of small impulse noise of electromagnetic nature, which are sometimes present in the signal. The appearance of such interference is explained by the location of the test benches of the Tver Institute of Railway Car Building on the territory of the Tver Railway Car Building Plant with a large number of welding and other energy-intensive equipment that "pollutes" the electromagnetic environment. Digital filtering was used to isolate the signal from the noise of the test equipment. As a result of a preliminary analysis, it was found that the main noise power is concentrated in the range below 50 kHz. In this case, the noise is also present at high frequencies, but the noise level decreases with increasing frequency. As a result, the frequency band was selected 165-185 kHz. A 4th order Chebyshev bandpass filter was used. An example of a filtering is shown in Fig. 4. In order to get rid of the noise of the loading device, the same filter was used in the Multi VirAnalyzer program during operational monitoring of the testing process. Tests have shown that along with the pulses accompanying the crack jumps, there is a secondary AE associated with the dynamic contacting of the crack faces. This signal, in contrast to the noise of the loading device, can be wide enough to make it difficult to distinguish it from the signals of a growing crack. The solution to this problem was proposed by Danegan and Harris [1]. They noticed that fatigue crack jumps occur only at a load close to the maximum load of the cycle. We called these signals primary. The crack signals, appearing as lesser loads and not directly related to crack growth, we called secondary. To isolate the primary crack signals, it is thus necessary to record the load profile simultaneously with the AE signals. To obtain information about the load profile, data from an ADXL345 (Analog Devices) accelerometer installed on the test object near the point of maximum deflection were recorded simultaneously with the registration of AE signals. Since the accelerometer readings are proportional to the variable component of the load ܲ ሺʹߨ݂ݐሻ (1), they allow to extract fragments of AE signals corresponding to the maximum load values: ܲ ܲǤ଼
(2)
286
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
where ��.� � �� � �.��� . The result is shown in Fig. 5. We considered the onset of crack growth to be the appearance of the first pulses that passed all the described filters with an amplitude of at least 0.1 V, in at least one channel.
Fig. 4. AE signal before (a) and after (b) applying a bandpass filter.
4. Determining the parameters of survivability The simplest indicator of survivability is the dimensionless parameter �: � � �� � ��� ���,
(3)
�� ��� � � �� �⁄� ��� .
(4)
where � is the number of load cycles to failure or loss of bearing capacity of the part, ��� is the number of cycles until a fatigue crack appears, detected visually or by other means. Denote the number of cycles until the fatigue ��� �� , and by the AE method – ��� . The parameters S calculated by these crack appears visually detected by ��� ��� �� numbers in accordance with (3) are denoted by � and � , respectively. To determine the correction factor of parameter survivability we used the expression:
Dmitriy Evseev et al. / Procedia Computer Science 149 (2019) 282–287 Author name / Procedia Computer Science 00 (2019) 000–000
287
The results of the tests and data processing are shown in Table 1.
Fig. 5. The result of the selection of the AE signal corresponding to the maximum load. Table 1. The results of the tests.
5 133 000
௩௦ ܰ
5 345 000
ܰ
10 000 000
ܵ ா
0,49
ܵ ௩௦
Correction
1
ா ܰ
0,47
4%
2
315 000
1 585 200
2 165 500
0,85
0,27
69%
3
8 300 000
9 080 300
10 000 000
0,17
0,09
46%
4
4 700 000
6 600 000
10 000 000
0,53
0,34
36%
5
1 630 000
2 025 500
3 561 500
0,54
0,43
20%
6
620 000
1 537 000
1 939 900
0,68
0,21
69%
7
1 280 000
1 380 000
1 763 000
0,27
0,22
21%
8
1 100 000
1 600 000
2 205 500
0,50
0,27
45%
9
3 800 000
4 150 000
8 100 000
0,53
0,49
8%
Frame
It follows from the table that the proposed method of recording and processing AE signals made it possible to clarify the vitality index in the direction of its increase from 4% to more than three times. 5. Conclusion The problem of cracks early detection by the acoustic emission method was solved during bench fatigue testing of rolling stock parts. A method for correctly determining the parameters of survivability during fatigue tests was proposed and tested. References [1] Harris, D.O. and H. L. Dunegan. (1974) “Continuous monitoring of fatigue-crack growth by acoustic-emission techniques.” Experimental Mechanics 14(2): 71–81. [2] Morton, T.M., R. M. Harrington, and J. G. Bjeletich. (1973) “Acoustic emissions of fatigue crack growth.” Engineering Fracture Mechanics 5 (3): 691-697. [3] Lindley, T.C., I. G. Palmer, and C. E. Richards. (1978) “Acoustic emission monitoring of fatigue crack growth.” Materials Science and Engineering 32 (1): 1-15. [4] Robertsa, T.M., and M. Talebzadehb. (2003) “Acoustic emission monitoring of fatigue crack propagation.” Journal of Constructional Steel Research 59 (6): 695-712