Machine Health Data Compression With Preserved Extrema Trends
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Solution Overview
Problem
Existing systems for storing machine health trend data over long periods face challenges in balancing memory requirements, as storing only average values loses extrema data, making it impossible to identify short-term peaks or determine if current levels have been reached before.
Innovation Solution
A method that calculates and stores average, maximum, and minimum values for incremental measurement time intervals, along with associated time values, to compress machine health data while preserving extrema details, reducing memory needs compared to storing raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If only average machine health parameter values are stored over a time period, then memory space requirements are reduced, but extrema information is lost making it impossible to identify short-term peaks or determine if current levels have been reached before
Solution Approach 1:
The patent extracts only the essential extreme values (maximum and minimum) from the raw machine health data within each time period, storing these extracted extrema alongside average values. This selective extraction preserves critical information about short-term peaks and valleys while significantly reducing the data volume compared to storing all raw measurements, thus resolving the contradiction between memory efficiency and information retention.
2Loss of information
If all raw machine health data including every measured value are stored, then complete information is preserved, but a significant amount of memory space is required
Solution Approach 1:
The patent creates a compressed representation (copy) of the raw machine health data by calculating and storing only the essential statistical features (average, maximum, and minimum values) for each time period. This compressed copy retains the critical information needed for trend analysis and extrema identification while occupying significantly less memory space than the complete raw dataset, effectively resolving the contradiction between information completeness and storage efficiency.
Data Source
AI summary
A computer-implemented method reduces an amount of machine health data to be stored in a data storage device while preserving details of extrema values occurring within incremental measurement time intervals in an extended time period during which the data were collected. The method includes: sensing an operational characteristic of a machine and generating an operational characteristic signal; generating machine health parameter data that include amplitude values and associated time values; for each incremental measurement time interval, calculating an average value of the amplitude values, identifying a maximum value of the amplitude values, and identifying a minimum value of the amplitude values; and storing a compressed machine health parameter data set in the data storage device. The compressed machine health parameter data set includes the calculated average values and the identified maximum and minimum values for the incremental measurement time intervals.