Selective Decimation and Analysis of Oversampled Data

摘要

Useful and meaningful machine characteristic information may be derived through analysis of oversampled digital data collected using dynamic signal analyzers, such as vibration analyzers. Such data have generally been discarded in prior art systems. In addition to peak values and decimated values, other oversampled values are used that are associated with characteristics of the machine being monitored and the sensors and circuits that gather the data. This provides more useful information than has previously been derived from oversampled data within a sampling interval.

主权项

1. A method for processing dynamic measurement data derived from an analog signal generated by an analog sensor in sensory contact with a machine or a process, the method comprising:
(a) generating the analog signal by the analog sensor in sensory contact with the machine or a process; (b) converting the analog signal into an oversampled digital data stream; (c) designating sampling interval datasets within the oversampled digital data stream; (d) analyzing at least a portion of the sampling interval datasets to determine one or more dataset attributes selected from the group consisting of a median value, a mode value, a standard deviation value (SDV), a maximum value, a range value, a minimum value, a root mean square (RMS) value, a statistical scatter value, a momentum value, a variance value, a skewness value, a kurtosis value, a peak shape factor (PSF) characteristic, a parametric-versus-causal (PvC) characteristic, and one or more difference values; (e) decimating sequential sampling interval datasets analyzed in step (d) to produce one or more scalar values corresponding to each sampling interval dataset; (f) generating a waveform comprising the scalar values produced in step (e); and (g) saving one or more of the one or more dataset attributes in association with one or more of the sampling interval dataset and the waveform.