Applications VI: Analysis of Random Data
Any attempt to enumerate the reasons for collecting data from random processes would necessarily be incomplete. Often conclusions can be drawn without any involved analysis; an experienced motorist can usually tell by “aural spectrum analysis” when the tappet clearance in his engine has increased just a few thousandth of an inch. Although a trained human operator has been a successful way of keeping complex sets of machinery under surveillance, we are now faced with the problems of fully automated engine rooms (for the understandable reason that trained operators are expensive items). Data on oil flows, steam pressures, bearing vibrations etc. have to be used to make decisions automatically. In some cases the penalty for a wrong decision is quite high, and then it is important to be sure as possible what valid conclusions can be drawn from the data. At this point the statistical nature of data analysis becomes evident. The reduced data based on an observation over a finite time T, i.e. an experiment, contains a component that could vary from sample to sample of length T, the remainder being what one really wants to know. For this reason it has been said that the major function of statistical procedures is to show us what we do not know from an experiment, not to tell us what we do know. This is far from being an admission of sterility, it serves as a warning about the misuse of random data.
KeywordsPower Spectrum Amplitude Distribution Random Data Random Vibration Engine Room
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Bibliography for Chapter 17
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