FlexPro Counting Procedures Option
Introduction
Besides the harmonic analysis by means of Fourier transformation, counting procedures have proved to be an important tool for examining signals, in particular for load-time functions.
Counting is based on a search for specific events in the load-time function, e.g. a certain load level being exceeded or a load alternation of a certain amplitude. For this purpose, the range of values of the load-time function is divided into discrete ranges, the classes. Each event found is assigned to a class and counted in this class.
The result shows a frequency for each class. The Count Option provides you with a broad spectrum of counting procedures. The bases of implementation are the counting procedures according to DIN 45667 and the more modern Rainflow counting procedure. The DIN 45667 procedure originated in the year 1969 and is oriented to the technical counting means (counting devices) available at the time. The Rainflow procedure has mainly replaced DIN 45667 procedures. Thus, only those DIN counting procedures were implemented for which the Rainflow procedure does not offer an equivalent. In addition to this, the Rainflow procedure caters better to the requirements of the operational reliability check. The Rainflow procedure provides better results here.
Features
- Markov matrix and Rainflow matrix in range-mean format and from-to format. The residue of the rainflow count can optionally be included into the result.
- Range filter to suppress small load changes.
- Class divisionsautomatic, start and width of class, begin and end, symmetrical or via external data set.
- Derived collectives: Peak and valley values, positive and negative ranges and range pairs, positive and negative level crossings.
- Frequencies: Absolute, relative, percentage and cumulative.
- Counting procedures according to DIN 45667: Sample, maximum value and dwell time.
- Compound counting procedures according to DIN 45667: Sample, maximum value and dwell time from two input datasets with separate class division for each data set.