If the process of condition monitoring of rolling bearings should be executed automatically to a large extend, a couple of specifications have to be considered for a reliable and early fault diagnosis. These concern the measuring-technical part of the signal extraction and the part of a bearing condition statement on basis of those won signals. Because of being proportional to the impact pulse during damage passing of the rolling elements the acceleration time signal seems to be suitable in terms of understandable measuring variable for diagnostics and should be the basic principle of this approach. Automated analyses can be performed with suitable experience by investigation of the time signal. This can help indicating future damages in case of unchanged surroundings since the time of the investigation. Here it should be tried to formalize the necessary decisions and to integrate them into a program sequence for bearing diagnostics. Such decisions concern the choice of the suitable frequency range, but also the parameters of other methods like the order of higher derivations and settings of a wavelet filter. Besides, an essential step of the formalization represents the deliberate use of Data Mining methods. Subsequently the rolling-bearing-diagnostic characteristics are considered with the creation of signal processing, feature generation, classification, evaluation criteria and searching algorithms. The experimental proof occurs with the help of an artificial outer ring damage of known geometry. The geometry-caused impulse is made visible by measuring the vibration signals at different contact angle situations, not only to show this context but to illustrate the effects of the signal processing.
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