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Vibration analysis applied to low-speed machines in the pulp and paper industry.

Application: Using integrated vibration analysis, it is possible to make an accurate diagnosis of the condition of bearings in low-based machines.

Predicting the presence of defects in the bearings of low-speed machines, using spectral analysis of vibration, can be difficult. The vibrations generated by these machines are of low amplitude compared with the level of electrical noise, and in many cases this prevents the detection of defects in bearings. In this paper, some real-world cases from the pulp and paper industry are used to illustrate that with (a) an integrated analysis of the vibration spectrum and waveform and (b) the use of averages and a fine frequency resolution, it is possible to detect defects in bearings of low-speed machines. A practical example showing the application of the demodulation technique on a roll of the third press of a paper machine is also presented.

Frequency (or spectral) analysis is the most common method used for detecting machine faults such as rotor unbalance, shaft misalignment, mechanical looseness, and bearing damage. The fundamental idea of frequency analysis is to find the relation between the spectral component frequencies and the frequencies of the dynamic forces producing the faults.

However, when the measured vibration signal is small, electronic and environmental noise can make the analysis of spectral data difficult, rendering the measurements useless. In these cases, the solution is to maximize the signal-to-noise ratio (SNR).

During data acquisition, it is possible to increase the SNR of the spectrum by increasing the number of averages and/or increasing the frequency resolution (more spectral lines).


We have shown that it is possible for field engineers and technicians to monitor the condition of low-speed machines by using integrated vibration analysis techniques and by paying strict attention to the selection and use of vibration-measurement equipment. Concerted efforts to improve the signal-to-noise ratio of the measurements are required.

Saavedra and Estupinan work at the University of Concepcion, Mechanical Engineering Dept., Casilla 160-C, Concepcion, Chile. Address correspondence to Saavedra by email at
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Title Annotation:Predictive Maintenance
Author:Estupinan, Edgar
Publication:Solutions - for People, Processes and Paper
Date:Jan 1, 2002
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