More In­focen­ter top­ics : Ther­moacous­ticsMeas­ure­ment tech­no­logy | Visu­al­iz­a­tion

In sig­nal tech­no­logy, the sampling fre­quency or sampling rate de­scribes the fre­quency used to sample a time-con­tinu­ous ana­log sig­nal at dis­crete points in order to gen­er­ate a time-dis­crete di­git­al sig­nal.

The IfTA AD4 mod­ules allow sampling rates up to 102.4 kHz.

The sampling the­or­em, namely the Nyquist-Shan­non sampling the­or­em, is a fun­da­ment­al the­or­em in tele­com­mu­nic­a­tions, sig­nal pro­cessing and in­form­a­tion the­ory. It deals with the ques­tion to what ex­tent an ana­log out­put sig­nal can be re­con­struc­ted without any er­rors from a time-dis­crete di­git­ized sig­nal. In order to be able to meas­ure a sig­nal, the sampling fre­quency must be at least twice the max­im­um fre­quency of the ana­log sig­nal to be meas­ured. Other­wise, the so called ali­asing oc­curs.

In sig­nal pro­cessing, anti-ali­asing means the act­ive pre­ven­tion or re­duc­tion of ali­asing ef­fects. Ali­asing ef­fects occur mainly when the sampled sig­nal has high­er fre­quen­cies than half the sampling fre­quency. This ef­fect is ex­pressed by the fact that fre­quen­cies high­er than half the sampling fre­quency are wrongly in­ter­preted as lower fre­quen­cies. To pre­vent ali­asing in tech­nic­al ap­plic­a­tions, low-pass fil­ters are used to fil­ter out fre­quen­cies above the half sampling fre­quency be­fore sampling.

The IfTA AD4 and AD32 mod­ules con­sist­ently im­ple­ment anti-ali­asing with ana­log and di­git­al fil­ters.

At high sampling rates and many chan­nels, large amounts of data ac­cu­mu­late with­in a short peri­od of time which can hardly be stored for longer peri­ods of time nor ana­lyzed. For ex­ample, only the max­im­um amp­litude with­in one minute can be stored, or only the max­im­um amp­litude across all chan­nels can be stored at one time in­stead of stor­ing each chan­nel.

The IfTA DataHub real­izes this data ag­greg­a­tion and al­lows to make large amounts of data vis­ible by means of small com­pact over­view files.

The Fast Four­i­er Trans­form­a­tion is a par­tic­u­larly fast and ef­fi­cient al­gorithm for cal­cu­lat­ing the dis­crete Four­i­er Trans­form­a­tion (DFT). Di­git­ized time-dis­crete sig­nals can be con­ver­ted into their in­di­vidu­al fre­quency com­pon­ents by the Four­i­er trans­form for fur­ther ana­lys­is.

The IfTA Sig­nalMin­er Firm­ware, which runs in real time on the DSP, and IfTA TrendView­er provide the FFT func­tion­al­ity.

What is the ad­vant­age of com­bin­ing on­line & off­line data ana­lys­is?

By com­bin­ing the ana­lys­is of off­line and on­line data in one pro­gram it is pos­sible to com­pare cur­rently meas­ured vi­bra­tion para­met­ers of the mon­itored ma­chine with already ex­ist­ing and re­leased meas­ure­ment data. In ad­di­tion to the dy­nam­ic data of the rotor dy­nam­ics, slower pro­cess vari­ables can also be dis­played in the same plot. Thus com­plex scen­ari­os can be ana­lyzed more ef­fi­ciently. On the hard­ware side, both slower meas­ure­ment cards and mod­ules for bus con­nec­tion are avail­able.

The IfTA Ar­gusOMDS sets stand­ards for the de­tailed and com­plete ana­lys­is of the vi­bra­tion be­ha­vi­or of ro­tors.

An order in ro­tat­ing ma­chines is defined as a mul­tiple of the ro­ta­tion­al speed (1st order = 1 x ro­ta­tion­al speed, 2nd order = 2 x ro­ta­tion­al speed, ..., n x ro­ta­tion­al speed). The order ana­lys­is ex­am­ines order-de­pend­ent phe­nom­ena such as un­bal­ance or fric­tion over a changed ro­ta­tion­al speed.

For order track­ing, a sig­nal with dis­crete val­ues de­pend­ing on the an­gu­lar po­s­i­tion of the wave is cal­cu­lated using res­ampling tech­niques. This sig­nal is trans­formed via FFT into the order do­main in­stead of the fre­quency do­main. There­fore, there is no smear­ing of order-de­pend­ent phe­nom­ena and they can be iden­ti­fied and re­solved more eas­ily.

IfTA TrendView­er of­fers order track­ing as an op­tion­al cal­cu­la­tion.

In res­ampling, a dis­crete sig­nal is re­writ­ten to other time-dis­crete data points. It is mainly used as a pre­par­a­tion for order track­ing, where a sig­nal is res­ampled de­pend­ing on the an­gu­lar po­s­i­tion of the wave.

A trig­ger is defined as a con­di­tion that ini­ti­ates fur­ther ac­tions in the meas­ur­ing sys­tem. The con­di­tion can be defined both with­in the meas­ur­ing sys­tem or ex­tern­ally. An ex­ample for this is the stor­age of data as soon as a limit value is ex­ceeded. Such limit val­ues can usu­ally be defined for known events. If, how­ever, in par­tic­u­lar sporad­ic events for which no trig­ger can yet be defined, con­tinu­ous stor­age is prefer­able, oth­er­wise the events will not be re­cor­ded if the limit val­ues are too high. Fur­ther ap­plic­a­tion ex­amples are the manu­al trig­ger­ing to take meas­ur­ing points, the stor­age of tran­si­ent op­er­at­ing points with high­er res­ol­u­tion than con­stant op­er­at­ing points (RunUp, Run­Down vs. Steady State for ro­tors), as well as the stor­age of un­ex­pec­ted events such as re­liefs or emer­gency shut­downs of ma­chines.