Mobile Machine Data Correction via Context-Value Separation
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Solution Overview
Problem
Machine-generated data from mobile machines is prone to errors due to environmental factors like dust, debris, and electromagnetic interference, leading to inaccurate or corrupted data.
Innovation Solution
A data correction system that divides data into context and value components, checks for container integrity and data quality, quarantines inaccurate data, and applies correction logic to produce verified data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If sensors operate in harsh mobile machine environments with dust, debris, and electromagnetic interference, then the machine can collect data from various sensors, but the data becomes corrupted or inaccurate
Solution Approach 1:
The system performs preliminary validation checks on data before it is fully processed or stored. Sensors and systems validate data format, range, and plausibility immediately upon generation, preventing corrupted data from propagating through the system. This preliminary action ensures data quality while maintaining collection in harsh environments.
Solution Approach 2:
The patent introduces intermediary validation layers between the sensors and the final data usage. These intermediaries include data buffers, validation routines, and correction algorithms that filter and clean sensor data, removing the harmful effects of environmental interference while preserving the underlying information.
2Reliability
If the system implements comprehensive data validation and correction processes, then data accuracy improves, but system complexity increases
Solution Approach 1:
The validation and correction system is divided into separate, modular components: format validation, range checking, plausibility validation, and correction algorithms. Each component handles a specific aspect of data quality, making the overall system more manageable and maintainable despite the comprehensive nature of the validation process.
Solution Approach 2:
The system includes self-diagnostic and self-correction capabilities where sensors and data processing routines automatically detect and correct their own errors without external intervention. This reduces the need for complex external validation systems while maintaining high data accuracy.
3Reliability
If data is validated and corrected in real-time, then data quality improves, but processing time increases
Solution Approach 1:
The system implements periodic validation at critical checkpoints in the data processing pipeline rather than continuous validation at every stage. Data is validated at generation, at transfer points, and before final usage, providing quality assurance while minimizing processing delays through strategic timing of validation operations.
Solution Approach 2:
Data format and basic validity checks are performed preliminarily and concurrently with data collection, so that obvious errors are identified immediately without adding sequential processing time. This preliminary filtering allows more intensive validation to focus only on data that passes initial checks.
Data Source
AI summary
Machine-generated data is divided into its metadata (or contextual data) that is indicative of a data container, and the data values themselves that are indicative of sensed variables. The metadata is checked to obtain a container integrity indicator of whether the data is accurate based on characteristics of the data container. The actual data values are subjected to quality checking to determine whether they have been corrupted by inaccuracies. Data corresponding to inaccurate metadata or inaccurate data values themselves is quarantined, and correction logic attempts to correct any inaccurate data. Corrected data is output with the verified data.