This disclosure relates to systems and methods for adaptively compressing data based on compression parameters. In one embodiment, a method for compressing a dataset is disclosed, including filtering a dataset based on occurrence of an event, and determining a quality of information index indicating a measure of quality of the dataset based on a quality of information estimation function. The method may include comparing the quality of information index with a list of indices stored in a lookup table to identify a target quality of information index and corresponding compression parameters, wherein the target quality of information index is indicative of a reference measure of quality of the dataset applicable for deriving statistical inferences based on analysis of the dataset. Also, the method may include inputting the compression parameters in a compression algorithm for compressing the dataset to achieve the target quality of information index for the analysis.

A device for processing rotation-dependent measured values includes a data converter, a sequencing control, and an output interface. Series of measured values, which are a function of the rotation of a shaft and of which at least one is an angle value that indicates the angular position of the shaft, are conveyable to the data converter at constant time intervals of a measuring interval. The data converter is adapted to subdivide a rotation of the shaft into sectors and, using one of the angle values as a reference angle value, to allocate received measured values to a sector, and per rotation of the shaft, to ascertain for each series of measured values precisely one result value for each sector. The result values are able to be output to the output interface.

The present invention makes it possible to detect an approximation error amount in approximating and encoding axis-dependent data that depends on the coordinate value of each axis of an industrial machine. An approximation error detection device 1 comprises an approximation error amount detection unit 11 that detects an approximation error amount with an absolute value greater than or equal to a predetermined threshold among approximation error amounts in performing model approximation encoding of axis-dependent data on the basis of a part of the axis-dependent data that depends on the coordinate value of each axis of an industrial machine, and on a linear combination model that approximates the axis-dependent data as a linear combination of data on each axis of the industrial machine.