• Ei tuloksia

Measurement challenges and sensor performance

Although the primary objective of the work was to investigate the acoustic phenomena in power semiconductors, also measurement issues had to be addressed. Since no previous work on acoustic emissions in the context of power semiconductor modules could be found, there were no reference measurement methods available, neither were there any purpose-built sensors in the market. As such, the experiments provided information not only on the nature of the acoustic phenomena but on how the acoustic emission sensors meet the requirements of performance.

Publications I and II present the same experiment carried out with different sensors. As de-scribed in section 3.1, both experiments support the same conclusion. Publication IV further explores the differences and challenges of the measurements.

3.3 Measurement challenges and sensor performance 33

By comparing an acoustic emission recorded by using two different sensors (Figure 3.2), one can immediately see that the resulting waveforms deviate considerably from each other. The KRN sensor captures much lower frequencies than the Kistler sensor. The higher frequencies present in the output of the Kistler sensor, however, are absent from the KRN sensor signal.

The frequency range of the KRN sensor covers that of the Kistler sensor entirely, and extends to the low frequencies, and up to a megahertz. Therefore, it would be reasonable to expect the KRN sensor to capture all the frequencies that the Kistler sensor does. One explanation for this might be resonances inside the sensors, the frequencies of which would depend on the construction of the sensors. The resonances may be caused by the mechanical elements in the sensors, or by the electrical parts in them.

Another possible source of resonances is the mechanical construction in which the acoustic emissions are measured. This encompasses the device under test, but also other parts of the experimental setup. None of the experiments presented in this work have the sensor directly connected to the DUT, but a metal object acoustically connecting the sensor to the DUT.

This acoustic environment may produce resonances of its own and also attenuate the acoustic signal to some degree.

There is also a distinct difference between the sensors during the IGBT pulse. The KRN sensor captures what looks like an interrupted sine wave during the pulse whereas the Kistler sensor fails to capture anything at that time.

35

Chapter 4

Conclusions

This work presents observations of acoustic emission in power semiconductor modules. The main result is that acoustic emission has now been shown to be a real phenomenon associated with power modules. Acoustic events related to the switching and failure of power modules were recorded. Three distinct types of acoustic emission were identified.

Results relevant to the instrumentation and measurement of acoustic phenomena are also dis-cussed. It was found that the wide-band acoustic emission sensors were capable of detecting the acoustic emissions presented in this work. The sensitivity of the commercially available sensors was adequate for the measurements presented.

In Publication I, it was shown that the switching transient in a power semiconductor module causes an acoustic emission. An acoustic wave propagates through the cooling surface of the semiconductor module and is detected by an acoustic emission sensor. In Publication II, this result was confirmed by using a different type of acoustic emission sensor in the same experimental setup. Although significant differences between the outputs of the sensors were observed, the results support the same conclusion: the switching operation of a power semi-conductor module causes an acoustic emission. The physical phenomenon that causes the acoustic emission is presently unknown.

In Publication III, IGBTs were made to fail, and the related acoustic emissions were moni-tored. Two distinct types of acoustic emission were observed: an immediate acoustic emis-sion that occurs at the time of failure, and post-failure acoustic emisemis-sions that occur tens of milliseconds after the failure. The fact that there are two distinct emission types suggests that two physical mechanisms may be causing the emissions. The mechanisms themselves are presently unknown. It should also be kept in mind that because of the sensor limitations, this experiment was conducted under unrealistic operating conditions, and therefore, the results cannot be directly generalized to apply to most semiconductor failures.

In Publication IV, questions related to the measurement and instrumentation in this study

were addressed. Several difficulties in the measurements were identified, the most important being the temperature limitations and the self resonance of the sensors. The publication also discusses the ways in which those limitations have been circumvented in this work.

Although the results presented in the publications are reliable, there are some shortcomings that hinder their generalizability. In the experiments, the modules are operated quite far from their nominal operating point. In the switching experiments, the modules are operated under voltages that are about 5% of the usual working voltage of the modules in question.

Furthermore, the switching experiments were conducted in room temperature, while in a typical converter application the operating temperature would be much higher.

A switching experiment was also conducted at a DC link voltage of 600 V. A similar but not identical waveform was observed in this case. It seems that multiple mechanisms may be causing acoustic emissions; some seem to be excited by current and some by voltage.

In some applications it can be beneficial if different acoustic phenomena can be triggered independently from each other.

Moreover, the mechanical construction used in all experiments was simplified; nevertheless, the experiments were able to demonstrate that acoustic emission originates from the power semiconductor module under test. While this would probably hold true in a typical power converter, such a converter can be expected to be acoustically more complicated. There are several other components besides the power semicondcutors that may contribute to the overall acoustic signal. Even if these other components do not act as an acoustic source, they may still interfere with the acoustic conduction path of the signal, altering the observed signal.

As stated in the introduction, the main application of this work is condition monitoring of power electronics systems. The results presented in the work cannot be directly applied to devise a condition monitoring method, or to deduce whether an acoustic-emission-based condition monitoring method is feasible. It does, however, provide a starting point for future work, which may ultimately lead to a condition monitoring method. This is the scientific impact of this work.

The work presented in this doctoral dissertation does not have a direct societal impact. Future work, on the other hand, may have a considerable societal impact in the form of enhanced availability of power electronics. If a condition monitoring method can be developed, it has the potential to promote the availability, quality of service, and safety in industrial plants, energy production, distribution of electricity, and transportation, to name but a few.

4.1 Considerations on future work

Based on the work presented in this doctoral dissertation, multiple paths are available for future research (Fig. 4.1). The two main paths are related to the physics of the acoustic phenomena and the condition monitoring application. The study of physics could focus on grasping the mechanisms behind the acoustic emissions and phenomena associated with the

4.1 Considerations on future work 37

propagation of the acoustic wave. Comprehension of these aspects would facilitate the de-velopment of practical applications. It would also be the key scientific impact of the research area. The effects of aging on the acoustic phenomena of the power modules should also be studied in order to support the development of a condition monitoring method.

In order to fully exploit the acoustic phenomena for condition monitoring, there is more to study than the mere effects of aging. Unless a very simple solution to the condition moni-toring proves to be feasible, considerable research efforts are required on detection methods, for instance algorithms that can discriminate aging information from other acoustic signals and interference. At this point it is, however, difficult to say what questions would have to be answered.

In addition to the physics and methods questions of condition monitoring, there are some very practical multidisciplinary problems that have to be solved before an end-user can ben-efit from any condition monitoring: would the condition monitoring system be permanently installed in the power converters, or would a portable instrument be used to periodically check the converter? Would the end-users of the converters own the condition monitoring system, or would they prefer to purchase condition monitoring as a service? How should the instrumentation be implemented? What kind of infrastructure such as data centers and data transmission would be needed?

The measurement-related aspects of acoustic emission are also an important, supporting re-search task. As has already been discussed in this work, the current measurement tools are not perfectly suited for this type of research. The measurement requirements of a practical condition monitoring method may significantly differ from those of the research presented in this work, meaning that it may be possible to sacrifice bandwidth in order to extend the tem-perature range of the instrumentation. Concerning a practical instrumentation, affordability is also an important factor.

One potential path is the study of nuisance noise caused by the power semiconductors. The results from this field could help improve the comfort of users in household systems, electric vehicles, and other applications where a regular user is in the vicinity of a power converter.

Figure 4.1. Future work on the subject can take several paths, some of the main ones being depicted here.

REFERENCES 39

References

Berth, M. (1998), “Partial discharge behaviour of power electronic packaging insulation,” in Electrical Insulating Materials, 1998. Proceedings of 1998 International Symposium on, pp. 565–568.

Breit, F., Dutarde, E., Saiz, J., Lebey, T., Malec, D., and Dinculescu, S. (2002), “Partial discharge detection in power modules,” inPower Electronics Specialists Conference, 2002.

pesc 02. 2002 IEEE 33rd Annual, vol. 2, pp. 748–752 vol.2.

Brown, D., Abbas, M., Ginart, A., Ali, I., Kalgren, P., and Vachtsevanos, G. (2012), “Turn-off time as an early indicator of insulated gate bipolar transistor latch-up,”IEEE Transactions on Power Electronics, vol. 27, no. 2, pp. 479–489.

Bua-Nunez, I., Posada-Roman, J.E., Rubio-Serrano, J., and Garcia-Souto, J.A. (2014), “In-strumentation system for location of partial discharges using acoustic detection with piezo-electric transducers and optical fiber sensors,”IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 5, pp. 1002–1013.

Colak, Ömer.H., Destici, T.C., Özen, ¸S., Arman, H., and Çerezci, O. (2009), “Detection of p- and s-wave arrival times using the discrete wavelet transform in real seismograms,”The Arabian Journal for Science and Engineering, vol. 34, no. 1A, pp. 79–89.

Eade, G. (1998), “Financial implications and cost justification,” in ed. A. Davies (ed.), Hand-book of Condition Monitoring: Techniques and Methodology, Chapman & Hall, Padstow, Cornwall, Great Britain.

Hafez, A.G., Khan, M.T.A., and Kohda, T. (2010), “Clear p-wave arrival of weak events and automatic onset determination using wavelet filter banks,”Digital Signal Processing, vol. 20, no. 3, pp. 715–723.

Jamshidi, F. and Shaabany, A. (2011), “Neural network and wavelet transform for classifica-tion and object detecclassifica-tion,”Journal of American Science, vol. 7, no. 5, pp. 20–25.

Kaewkongka, T. and Lim, J. (2007), “Statistical estimated parameter for pipeline condition monitoring using acoustic emission,” inIEEE Instrumentation and Measurement Technol-ogy Conference Proceedings, IMTC 2007, pp. 1–3.

Kaewkongka, T., Lim, J., Kanjanaprayut, N., and Daopiset, S. (2008), “Corrosion monitoring using acoustic emission and potentiodynamic method,” inProceedings of IEEE Instrumen-tation and Measurement Technology Conference, IMTC 2008, pp. 717–719.

Kärkkäinen, T. and Silventoinen, P. (2013), “Considerations for active condition monitoring in power electronic converters,” in15th European Conference on Power Electronics and Applications (EPE), pp. 1–5.

Lamonaca, F., Carrozzini, A., Grimaldi, D., and Olivito, R.S. (2012), “Acoustic emission monitoring of damage concrete structures by multi-triggered acquisition system,” inIEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp.

1630–1634.

Lebey, T., Dinculescu, S., and Malec, D. (2004), “Partial discharges testing of power mod-ules,” inSolid Dielectrics, 2004. ICSD 2004. Proceedings of the 2004 IEEE International Conference on, vol. 2, pp. 896–899 Vol.2.

Lehmann, J., Netzel, M., Herzer, R., and Pawel, S. (2003), “Method for electrical detection of bond wire lift-off for power semiconductors,” inProceedings of IEEE 15th International Symposium on Power Semiconductor Devices and ICs, ISPSD ’03, pp. 333–336.

Li, R. and He, D. (2012), “Rotational machine health monitoring and fault detection using emd-based acoustic emission feature quantification,”IEEE Transactions on Instrumenta-tion and Measurement, vol. 61, no. 4, pp. 990–1001.

Lim, J. and Kaewkongka, T. (2007), “Micro cracking in stainless steel pipe detection by using acoustic emission and crest factor technique,” inProceedings of IEEE Instrumentation and Measurement Technology Conference Proceedings, IMTC, pp. 1–3.

Martins, C.H.R., Aguiar, P.R., Frech, A., and Bianchi, E.C. (2014), “Tool condition moni-toring of single-point dresser using acoustic emission and neural networks models,”IEEE Transactions on Instrumentation and Measurement, vol. 63, no. 3, pp. 667–679.

Moschioni, G., Saggin, B., and Tarabini, M. (2004), “Sound source identification using co-herence and intensity based methods,” inProceedings of the 21st IEEE Instrumentation and Measurement Technology Conference, IMTC 04., vol. 3, pp. 1959–1964 Vol.3.

Navarro, L., Delgado, M., Urresty, J., Cusido, J., and Romeral, L. (2010), “Condition mon-itoring system for characterization of electric motor ball bearings with distributed fault using fuzzy inference tools,” inProceedings of the IEEE Instrumentation and Measure-ment Technology Conference (I2MTC), pp. 1159–1163.

Noma, H., Tabaru, T., Akiyama, M., Miyoshi, N., Hayano, T., and Cho, H. (2007), “High-temperature acoustic emission sensing using aluminum nitride sensor,”Journal of Acoustic Emission, vol. 25, pp. 107–114.

Ribrant, J. and Bertling, L. (2007), “Survey of failures in wind power systems with focus on Swedish wind power plants during 1997 – 2005,”IEEE Transactions on Energy Conver-sion, vol. 22, no. 1, pp. 167–173.

Smulko, J., Józwiak, K., Olesz, M., and Hasse, L. (2011), “Acous-tic emission for detecting deterioration of capacitors under aging,”

Microelectronics Reliability, vol. 51, no. 3, pp. 621 –627, URL http://www.sciencedirect.com/science/article/pii/S0026271410005391.

REFERENCES 41

Stenberg, A. and Holttinen, H. (2011), “Wind energy statistics of Finland – yearly report 2010,”VTT Working papers, vol. 178.

Suprijanto, Istiana, T., Hariyanto, and Dian, N.A. (2013), “Detection of p-wave on broadband seismometer using discrete wavelet denoising,” inProceedings of the 3rd International Conference on Instrumentation Control and Automation (ICA), pp. 110–114, iD: 1.

Turkia, V. and Holttinen, H. (2013), “Wind energy statistics of Finland – yearly report 2011,”

VTT Technology, vol. 74.

Veleshchuk, V., Lyashenko, O., Myagchenko, Y., and Chuprina, R. (2004), “Evolution of the electroluminescence spectra and the acoustic emission of the epitaxial structures of GaAsP,”Journal of Applied Spectroscopy, vol. 71, no. 4, pp. 553–557.

Vlasenko, A., Lyashenko, O., Oleksenko, P., and Veleschuk, V. (2008), “Fluctuations of cur-rent, electroluminescence and acoustic emission in light-emitting A3B5heterostructures,”

Semiconductor Physics, Quantum Electronics & Optoelectronics, vol. 11, no. 32, pp. 230–

235.

Zhou, S., Zhou, L., and Sun, P. (2013), “Monitoring potential defects in an igbt module based on dynamic changes of the gate current,”IEEE Transactions on Power Electronics, vol. 28, no. 3, pp. 1479–1487.

Zhu, P., Liu, Y., Robert, R., and Hao, X. (2011), “Offshore wind converter reliability evalua-tion,” inIEEE 8th International Conference on Power Electronics and ECCE Asia (ICPE ECCE), pp. 966–971.

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