Field Device Reliability Data Compression With Accuracy Thresholds
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Solution Overview
Problem
The challenge of assessing the reliability of field devices under variable conditions is exacerbated by data scarcity and the need for extensive testing due to diverse environmental variables, leading to inefficiencies and potential inaccuracies in reliability assessment.
Innovation Solution
A method involving dynamic data compression based on environmental and usage conditions, where reliability and hazard values are calculated with and without compression, compared against predefined thresholds, and data storage is halted when accuracy thresholds are exceeded, ensuring accurate reliability assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from a large number of devices is stored over an extended period to assess reliability under variable conditions, then the reliability assessment accuracy is improved, but the data volume and storage requirements increase exponentially
Solution Approach 1:
The patent segments the massive dataset by dividing it into device-specific subsets and time-based intervals. Each device's data is processed independently through the compression algorithm, and the overall reliability assessment is synthesized from these segmented results. This allows manageable processing of large-scale data while maintaining comprehensive coverage across multiple devices and time periods.
Solution Approach 2:
The patent extracts and stores only the essential reliability-related features and compressed representations of environmental/operational data, rather than preserving all raw measurement points. By identifying and retaining only the critical data elements needed for reliability assessment, the system achieves accurate results with significantly reduced storage requirements.
2Quantity of substance
If data compression is applied to reduce storage requirements, then the storage efficiency is improved, but the accuracy of reliability assessment may deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the compression algorithm continuously monitors the impact of compression on reliability assessment results. When the assessment accuracy falls below a predefined threshold, the system adjusts compression parameters or increases data retention levels. This closed-loop control ensures that compression efficiency is optimized while maintaining acceptable accuracy standards for reliability evaluation.
Solution Approach 2:
The patent dynamically adjusts compression parameters such as data sampling intervals, aggregation levels, and feature selection criteria based on the specific characteristics of the devices and environmental conditions being monitored. By adapting these parameters to match the actual data patterns and reliability assessment requirements, the system achieves high compression ratios without sacrificing assessment accuracy.
3Measurement precision
If the complete dataset is retained without compression, then the reliability assessment accuracy is maintained, but the computational effort and processing time increase
Solution Approach 1:
The patent performs preliminary compression and feature extraction on the raw data before the actual reliability assessment computation. By pre-processing the data to extract essential characteristics and reduce dimensionality, the system eliminates redundant computational operations during the assessment phase, significantly reducing processing time while preserving the information needed for accurate reliability evaluation.
4Adaptability or versatility
If data from diverse environmental conditions is collected to improve assessment robustness, then the applicability of the assessment is improved, but the complexity of the system increases
Solution Approach 1:
The patent implements a universal data compression and processing framework that can handle multiple types of devices, environmental conditions, and operational scenarios through a single standardized system. The compression algorithm and reliability assessment model are designed to be device-agnostic and condition-adaptive, allowing the same system architecture to serve diverse applications without requiring complex device-specific configurations or multiple specialized systems.
Data Source
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AI summary
The invention relates to a computer-implemented method for assessing the reliability of field device data comprising the steps: starting (100) an individual cycle; receiving (102) reliability-related data from field devices; calculating (104) and accumulating a first reliability value and/or a first hazard value over a yet undetermined time span without compression according to a model that uses at least one variable representing the reliability-related data; calculating (106) and accumulating a second reliability value and/or a second hazard value over the yet undetermined time span with compression according to the model, which uses the at least one variable representing the reliability-related data, wherein the values of the at least one variable are compressed; comparing (108) the first and the second reliability and/or second hazard values to a related pre-defined threshold corresponding to an accuracy; if exceeding the threshold (110), stopping (112) the calculation of the first and the second reliability and/or hazard values and determining the time span; and storing (114) the compressed values of the at least one variable; and closing (116) the individual cycle.