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. 2022 Jun 29;22(13):4932.
doi: 10.3390/s22134932.

A Novel Method of Impeller Blade Monitoring Using Shaft Vibration Signal Processing

Jindrich Liska  1 , Vojtech Vasicek  1 , Jan Jakl  1
Affiliations

A Novel Method of Impeller Blade Monitoring Using Shaft Vibration Signal Processing

Jindrich Liska et al. Sensors (Basel). .

Abstract

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.

Keywords: algorithm; diagnostics; impeller blade; monitoring; signal processing; steam turbine; vibration.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Low-pressure turbine scheme with the sensor location of BTT sensors and standard shaft vibration sensors.
Figure 2
Figure 2
Spectrogram of shaft vibration signal from low-pressure turbine (left) with highlighted LSB and USB components (right).
Figure 3
Figure 3
Flowchart of the developed method for online long-term blade vibration monitoring.
Figure 4
Figure 4
TG 250 MW—Filtering of identified components in the spectrum.
Figure 5
Figure 5
TG 250 MW—Discrete Fourier transform amplitude spectrum of the shaft vibration signal and its spectral envelope—liftered spectrum.
Figure 6
Figure 6
TG 250 MW—Filtered Discrete Fourier transform amplitude spectrum of the shaft vibration signal.
Figure 7
Figure 7
TG 250 MW—Time-averaged filtered amplitude spectrum of the relative shaft vibration signal.
Figure 8
Figure 8
TG 250 MW—Blade component identification.
Figure 9
Figure 9
TG 250 MW—Amplitude spectrogram of the relative shaft vibration signal (left); averaged and filtered amplitude spectrogram of the same signal (right).
Figure 10
Figure 10
TG 250 MW—Identified blade vibration components in amplitude spectrogram of the relative shaft vibration signal (left); tracking of blade vibration components using the cluster analysis (right).
See this image and copyright information in PMC

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