The monitoring of impeller blade vibrations is an important task in the diagnosis of turbomachinery, especially in terms of steam turbines. Early detection of potential faults is the key to avoid the risk of turbine unexpected outages and to minimize profit loss. One of the ways to achieve this is long-term monitoring. However, existing monitoring systems for impeller blade long-term monitoring are quite expensive and also require special sensors to be installed. It is even common that the impeller blades are not monitored at all. In recent years, the authors of this paper developed a new method of impeller blade monitoring that is based on relative shaft vibration signal measurement and analysis. In this case, sensors that are already standardly installed in the bearing pedestal are used. This is a significant change in the accessibility of blade monitoring for a steam turbine operator in terms of expenditures. This article describes the developed algorithm for the relative shaft vibration signal analysis that is designed to run in a long-term perspective as a part of a remote monitoring system to track the natural blade frequency and its amplitude automatically.

NTIS New Technologies for the Information Society Faculty of Applied Sciences University of West Bohemia Univerzitni 8 301 00 Plzen Czech Republic

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Russhard P. Vibration Engineering and Technology of Machinery. University of Manchester; Manchester, UK: 2014. The Rise and Fall of the Rotor Blade Strain Gauge; pp. 27–37. DOI

Heath S., Imregun M. A Survey of Blade Tip-Timing Measurement Techniques for Turbomachinery Vibration. J. Eng. Gas Turbines Power. 1998;120:784–791. doi: 10.1115/1.2818468. DOI

Mathioudakis K., Loukis S., Papiliou K. Casing Vibration and Gas Turbine Operating Conditions. American Society of Mechanical Engineers (ASME); Toronto, ON, Canada: 1989. DOI

Mathioudakis K., Papathanasiou A., Loukis E., Papiliou K. Fast response wall pressure measurement as a means of gas turbine blade fault identification. J. Eng. Gas Turbines Power. 1991;113:269–275. doi: 10.1115/1.2906558. DOI

Gubran A.A., Sinha J.K. Shaft instantaneous angular speed for blade vibration in rotating machine. Mech. Syst. Sig. Process. 2014;44:47–59. doi: 10.1016/j.ymssp.2013.02.005. DOI

Bachschmid N., Salvini G., Tanzi E., Pesatori E. Proceedings of the 9th IFToMM Intarnational Conference on Rotor Dynamics. Springer; Cham, Switzerland: 2015. The Influence of Blade Row Dynamics on Lateral and Torsional Shaft Vibrations in Steam Turbines. DOI

Vasicek V., Liska J., Strnad J., Jakl J. Identification of dynamic behavior of steam turbine blades using rotor vibration measurement; Proceedings of the 14th European Conference on Turbomachinery Fluid dynamics & Thermodynamic, ETC 2021; Gdansk, Poland. 12–16 April 2021.

Vasicek V., Liska J., Strnad J., Jakl J. Advances in Condition Monitoring and Structural Health Monitoring. Springer; Singapore: 2021. Experimental validation of the blade excitation in a shaft vibration signals. DOI

Liska J., Jakl J., Vasicek V. Rotating blades monitoring using standard turbine instrumentation. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2019;233:7447–7458. doi: 10.1177/0954406219889084. DOI

Liska J., Vasicek V., Jakl J. On possibilities of using relative shaft vibration signals for rotating blades monitoring; Proceedings of the Turbomachinery Technical Conference and Exposition; Oslo, Norway. 11–15 June 2018; DOI

Randall R.B. Vibration-Based Condition Monitoring. John Wiley & Sons Ltd.; Hoboken, NJ, USA: 2011. DOI

Vaseghi S.V. Advanced Digital Signal Processing and Noise Reduction. John Wiley & Sons; Hoboken, NJ, USA: 2000. DOI

Proakis J.G., Manolakis D.G. Digital Signal Processing, Principles, Algorithms, and Applications. Prentice-Hall; Hoboken, NJ, USA: 1996. DOI

Prabhu K.M.M. Window Functions and Their Applications in Signal Processing. Taylor & Francis Group; Abingdon-on-Thames, UK: 2014. DOI

Deller J.R., Hansen J.H.R., Proakis J.G. Discrete-Time Processing of Speech Signals. Willey-IEEE Press; Hoboken, NJ, USA: 1999.

Everitt B.S., Landau S., Leese M., Stahl D. Cluster Analysis. 5th ed. John Wiley & Sons Ltd.; Hoboken, NJ, USA: King′s College; London, UK: 2011. DOI

Boyce M.P. Gas Turbine Engineering Handbook, 4th ed. Elsevier; Amsterdam, The Netherlands: 2011. DOI