State College, Pa., home to both The Pennsylvania State University and KCF Technologies, shares the same time zone with Chile's Universidad de Concepción,even though the two are about 5,400 miles apart from north to south. Still, it says a lot about 21st-century technologies in the Americas that both these esteemed institutions of higher learning and their neighboring industries share a passionate interest in predictive maintenance technologies.
Founded in 1915 as the Technical Association of the Pulp and Paper Industry, TAPPI is today a registered not-for-profit international organization of about 14,000 members--pulp and paper engineers, scientists, managers, and academics. Two such academics, Pedro Saavedra and Edgar Estupiñan of the Mechanical Engineering Department at UdeC, coauthored a paper in the May 2002 TAPPI Journal titled "Vibration analysis applied to low-speed machines in the pulp and paper industry." This peer-reviewed predictive maintenance column "used some real-world historical cases from the pulp and paper industries to illustrate that with an integrated analysis of the vibration spectrum and waveform, and the use of averages and a fine frequency resolution, it is possible to detect defects in bearings of low-speed machines." These are defined by them as machines operating at speeds from six to 300 cycles per minute.
"Most mills have been using the technique for some time to identify deterioration of vital equipment components...predicting and preventing catastrophic failures. However, monitoring of low-speed machines is more complicated than general machinery monitoring. In low-speed machines, the magnitude of the dynamic forces generating the machine vibrations decreases as the rotational speed of the machine decreases. In addition, low-speed machines are typically massive in size. Therefore, the resultant vibration on the bearing housing...is often very low and can be hidden by background noise."
The six-page article uses numerous images of vibration spectra and waveforms to show how the analysis was done, and how the detected low-frequency aberrations appeared in practical applications during the study. "Frequency (or spectral) analysis is the most commonly used method for detecting machines 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."
The authors conclude that "...research has 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 measurement are required."
More candid still are the "Insights" Saavedra and Estupiñan added following the article itself, including this: "Mill maintenance staff can use vibration analysis to predict faults, but the most important thing is the correct analysis of the information. Significant cost savings are possible through enhanced maintenance planning and fault prediction."
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