Fixed Extrapolation Error Data Simplification for Telematics
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Solution Overview
Problem
Telematics systems face challenges in accurately simplifying and transmitting data from assets, leading to inconsistent error profiles in value estimation calculations, which affects the reliability of asset status determination, especially when data is collected over time.
Innovation Solution
A fixed estimation error data simplification process that limits interpolation and extrapolation errors by using a vertical deviation approach, ensuring a consistent error bound across simplified data sets, and triggers data logging based on vertical deviations from trends, thereby maintaining error consistency and capturing more detailed data during rapid changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data simplification is applied to reduce data transmission and storage requirements, then data transmission efficiency is improved, but measurement precision deteriorates due to inconsistent error profiles in value estimation calculations
Solution Approach 1:
The patent changes the parameter of error measurement from perpendicular distance to vertical distance (deviation from trend line). This parameter transformation ensures that error bounds remain consistent during value estimation calculations even after data simplification, thereby maintaining measurement precision while still achieving data reduction for improved transmission efficiency.
Solution Approach 2:
The patent implements a feedback mechanism where the error profile from simplified data is continuously evaluated and used to adjust the simplification process. By monitoring whether vertical deviations exceed acceptable thresholds, the system can adaptively refine which data points to retain, ensuring that value estimation precision is maintained while maximizing data transmission efficiency.
2Device complexity
If traditional perpendicular distance method is used for data simplification, then data reduction is achieved, but reliability deteriorates due to inconsistent error profiles affecting asset status determination
Solution Approach 1:
The patent transforms the error measurement parameter from perpendicular distance to vertical distance (deviation from trend line). This change ensures that error profiles remain consistent even after aggressive data simplification, thereby maintaining the reliability of asset status determination while achieving significant data set reduction.
Solution Approach 2:
The patent segments the data simplification process into two distinct phases: (1) identifying and removing redundant data points based on vertical deviation from trend lines, and (2) preserving critical data points that maintain error consistency. This segmentation allows the system to achieve data reduction while safeguarding the reliability needed for accurate asset status determination.
3Measurement precision
If more data points are retained to maintain error consistency, then measurement precision is improved, but loss of time increases due to longer data processing and transmission duration
Solution Approach 1:
By changing the error measurement parameter to vertical distance from trend lines, the patent achieves error consistency with fewer data points compared to traditional perpendicular distance methods. This parameter transformation reduces the number of points that need to be retained and processed, thereby maintaining measurement precision while reducing data processing time.
Solution Approach 2:
The patent extracts and removes redundant data points that do not contribute to error consistency, keeping only the essential points needed to maintain measurement precision. This extraction approach minimizes the data set size requiring processing and transmission, reducing time loss while preserving error consistency.
4Productivity
If aggressive data simplification is applied to maximize transmission efficiency, then productivity is improved, but measurement precision deteriorates due to increased extrapolation errors
Solution Approach 1:
The patent changes the error measurement parameter to vertical distance from trend lines, which provides a more accurate representation of actual measurement deviations. This parameter transformation allows for more aggressive data simplification while maintaining extrapolation precision, as the vertical deviation metric better captures the true error profile even with reduced data points.
Solution Approach 2:
The patent implements feedback monitoring of vertical deviations to ensure that aggressive data simplification does not push extrapolation errors beyond acceptable thresholds. The system continuously evaluates whether removed data points would have significantly impacted extrapolation accuracy, allowing maximization of transmission efficiency while safeguarding measurement precision.
Data Source
AI summary
Methods and devices for simplifying data collected from assets are provided. An example method involves obtaining raw data from a data source at an asset, determining that a data logging trigger is satisfied by determining that a recently obtained point in the raw data differs from a corresponding predicted point predicted by extrapolation based on previously saved points included in one or more previously generated simplified sets of data by an amount of extrapolation error that is limited by an upper bound that is fixed as the raw data is collected over time, and, when the data logging trigger is satisfied, performing a dataset simplification algorithm on the raw data to generate a simplified set of data.