Abstract: A monitoring device for detecting a diagnostic state of an industrial machine on the basis of vibrations associated with the industrial machine, includes at least one measurement transducer for detecting at least one first signal characterizing the vibration of the industrial machine in a time period and for detecting at least one second signal characterizing the vibration of the industrial machine in the same time period, wherein the at least one measurement transducer has at least one signal processor for evaluating the at least first signal as a first characteristic value and for evaluating the at least second signal as a second characteristic value and wherein the at least one first characteristic value and the at least one second characteristic value form a characteristic value tuple, further having a storage unit, in which different diagnostic states describing the state of the industrial machine are saved, wherein the diagnostic states are determined as a function of the characteristic value tuple, and

Abstract: A monitoring device for detecting a diagnostic state of an industrial machine on the basis of vibrations associated with the industrial machine, includes at least one measurement transducer for detecting at least one first signal characterizing the vibration of the industrial machine in a time period and for detecting at least one second signal characterizing the vibration of the industrial machine in the same time period, wherein the at least one measurement transducer has at least one signal processor for evaluating the at least first signal as a first characteristic value and for evaluating the at least second signal as a second characteristic value and wherein the at least one first characteristic value and the at least one second characteristic value form a characteristic value tuple, further having a storage unit, in which different diagnostic states describing the state of the industrial machine are saved, wherein the diagnostic states are determined as a function of the characteristic value tuple, and

Abstract: The invention relates to a method for determining the root mean square value of a vibration variable that is measured on a machine. A test measurement is carried out in order to obtain a test signal of the vibration variable with a test signal length; the influence of shortening the signal length of the test signal on the associated root mean square value is estimated and the relative deviation of the root mean square value emerging for a shortened signal length from the root mean square value emerging for the full test signal length is estimated therefrom as a function of the signal length to obtain a root mean square value error function; and a measurement of the vibration variable is carried out for determining the root mean square value. The measurement duration of this measurement is selected on the basis of the root mean square value error function.

Abstract: The invention relates to a method for determining the root mean square value of a vibration variable that is measured on a machine. A test measurement is carried out in order to obtain a test signal of the vibration variable with a test signal length; the influence of shortening the signal length of the test signal on the associated root mean square value is estimated and the relative deviation of the root mean square value emerging for a shortened signal length from the root mean square value emerging for the full test signal length is estimated therefrom as a function of the signal length to obtain a root mean square value error function; and a measurement of the vibration variable is carried out for determining the root mean square value. The measurement duration of this measurement is selected on the basis of the root mean square value error function.

Abstract: The method provides determining at least one speed of a machine, wherein, on the basis of the measurement of a vibration variable carried out on the machine over time, the complex spectrum of this variable is determined, wherein a frequency interpolation is carried out; boundary conditions are established for the evaluation of the spectrum, which include the permissible frequency range of an expected main speed, a set of relative frequencies with respect to the main speed in the form of frequency multipliers, and a weighting factor for the particular relative frequency; a spectral probability density is calculated with consideration for the boundary conditions, which results for each frequency of the permissible frequency range as a sum of the amplitude of the spectrum, which has been weighted with the particular weighting factor and the main speed is determined on the basis of the frequency having the maximum probability density.