Figure 16-4b. .aterfall graph of increasing rpm
Table 16-1 Vibration .iagnosis
Usual .redominant Frenuency* .ause o. Vibration
Running frequency .oose assembly of bearingliner，at -4 %bearingcasing， or casing and support
.oose rotor shrink fits Friction-induced whirl Thrust bearing damage
Running frequency  Bearing-support excitation
at 4 -5 %  .oose assembly of bearingliner，bearingcase， or casing and support Oil whirl Resonant whirl
.learance induced vibration
Running frequency  Initial unbalance Rotor bow
.ost rotor parts
.asing distortion Foundation distortion Misalignment Piping forces Journal & bearing eccentricity Bearing damage Rotor-bearing system critical
.oupling critical Structural resonances Thrust bearing damage
Odd frequency  .oose casing and support Pressure pulsations
.ibration transmission Gear inaccuracy
.alve vibration

.ery high frequency  .ry whirl Blade passage
* Occurs in most cases predominantly at this frequency. harmonics may or may not exist.
evaluating future spectra.認hen a baseline signature is determined， it shouldbe carefully evaluated， and every component should be identified as far as possible.
First， and the most important factorto determine， is the primary orfundamental excitation frequency(i.e.， frequency of the forcing function). In certain machines more than one excitation corresponds to the running speed of the machine. In split-shaft and multispool machines there is more than one running speed.
The relationships in Table 16-1 help to further identify excitations. This information in con.unction with the baseline signature can identify the causes of sudden changes in the spectrum.However， this method runs into difficulty when a new machine is being brought up to speed. No baseline signature is available. Normal operation of the machine is not known. Information about similar machines is of limited value because of the wide variation between different samples of the same machine. This lack of knowledge is the most challenging aspect of machine vibration analysis.
For a new machine， the harmonic part of the spectrum is approximately known in its frequency content due to its relationship with the running speed. The amplitudes at these frequencies are not known. The subharmonicpart， with a lot of information unrelated to the runningspeed， is unknown both in frequency and amplitude content. To predict some characteristics ofthe subharmonic spectrum， transfer-function analysis is employed.
Transfer-function analysis consists of providing an external excitation of a known variable frequency by means of a vibrator. This excitation is applied to the machine while it is stopped. The observed vibration response is a measure of the machine"s structural characteristics. It helps in identifying various structural resonance frequencies and thus provides some informa-tion about the subharmonic spectrum.
.uring the startup of a new machine， one should try to identify all thema.or peaks in the real-time spectrum. If unidentifiable peaks appear， then perhaps the speed should be held constant until a cause for the peak isidentified.認hen a completely new component shows up on thespectrum， a baseline signature is of limited help in pinpointing the cause of such acomponent. Generally， such an occurrence is a warning of future disaster. Ifthe new component is erratically changing in time， it almost certainly spellstrouble. On the other hand， a low-amplitude， a broad-bandpeak， or a set of peaks that gradually build-up over years of operation may be the result of normal aging or the settling-down process and may be completely harmless. The identification problem area is again a matter of .udgment. Some insightcan be gained by studying published case histories， but manytimes， evenafter a ma.or failure， the cause of the failure cannot be positively identified.To properly utilize spectrum data as an analysis tool， one must use it in con.unction with performance factors.
　
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