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Baseline for .achinery
.echanical baseline. The vibration baseline for a machine can be defined as the normal or average operating condition of a machine. It can be represented on a vibration spectrum plot showing vibration frequency on the .-axis and vibration amplitude (peak-to-peak displacement， peak velo-city， or peak acceleration) on the .-axis. Since the vibration spectrum will be different atdifferent positions， the spectrum must be associated with a specific measurement position or sensor location on the machine. Whenportable vibration measurement equipment isused， it is essential to ensure that the sensor is relocated at exactly the same point on the machine each time vibration readings are taken. Changes of baseline with machine speedand process conditions should be investigated and， where necessary， baseline should be generated for set ranges of speeds and process conditions. Whenthe operating vibration levels exceed the baseline levels beyond setvalues， an alert signal should be activated for investigation of this condition.

.erothermal baseline.In addition to the vibration baselinespectrum， amachine also has an aerothermal performance baseline， or its normal oper-ating point on the aerothermal characteristic. Significant deviation of the operating point beyond its base point should generate alert signals.
When a compressor operates beyond itssurge margin， a danger alert should be activated. A typical compressor characteristic is presented in Figure 19-15. Some of the other monitoring and operating outputs are loss in compressorflow， loss in pressure ratio， and increase in operating fuelcost dueto， for instance， operating at off-design conditions or with a dirty compressor.

Since aerothermal performance of compressors and turbines is very sens-itive to inlet temperature and pressure variations， it is essential to normalizethe aerothermal performance parameters such asflow，speed， horsepower，etc.， to standard-day conditions. When these corrections to standard condi-tions are not applied， a performance degradation may appear to occur when in fact it was a performance change resulting merely from ambient pressure and temperature changes. Some of the equations for obtaining correction to standard-day conditions are given in Table 19-3.
.ata Trending
The data received should first be corrected for sensing errors. This usually consists of sensor calibration correction.

Table 19-3
.as Turbine .erothermal .erformance E.uations
for Correction to Standard-.ay Conditions

Factors for Correction to Standard-.ay Temperature & .ressure Conditions
Assumed standard-day pressure  14.7 psia 
Assumed standard-day temperature  60 0F (520 0.) 
Conditions of test 
Inlet temperature  Ti 0. 
Inlet pressure  .i psia 

Corrected compressor discharge temperature二 (.bserved temperature) (520/Ti)
Corrected compressor discharge pressure二 (.bserved pressure) (14.7/.i)
V古古古古古古古古古古古古古古古 Corrected speed二 (.bserved speed)520/Ti V古古古古古古古古古古古古古古古
Corrected air flow二 (.bserved flow) (14.7/.i)Ti/520
V古古古古古古古古古古古古古古古
Corrected horsepower二 (.bserved power) (14.7/.i)Ti/520

The trending technique essentially involves evaluating the slope of a curve derived from the received data. The slope of the curve is calculated for both along-termtrend， about 168 hours， and a short-termtrend， based on the last 24 hours. If the short-term slope deviates from the long-term slope beyond asetlimit， it means that the rate of deterioration ischanged， and the main-tenance schedule will be affected.Thus， the program might take into account the biasing of the long-term slope by the short-term slope. Figure 19-16 shows a schematic of this type of trending. .umerous statistical techniques are available for trending.
Trended data is used to obtain predictions that are helpful in the sched-uling of maintenance. .eferring to Figure19-17， for example， it is possible to estimate when compressor cleaning will be necessary. This figure was pre-pared by recording the compressor exit temperature and pressure each day.These points are thenjoined， and a dotted line is projected to predict when
　
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