Data Reconciliation Framework for Industrial Asset Analytics
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Solution Overview
Problem
Industrial predictive models often produce inaccurate results due to faulty or incomplete data, and existing frameworks lack effective methods for detecting and correcting such issues, leading to unreliable analytics for asset management.
Innovation Solution
A data reconciliation framework that detects inconsistent and incomplete data records, imputes missing data, and corrects errors by using a configurable system model to select appropriate heuristic and reconciliation techniques, leveraging external data sources to ensure accurate data for analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple industrial assets and sensors, then the quantity and variety of data increases, but data inconsistency and incompleteness increase
Solution Approach 1:
The patent segments the data validation process into multiple independent heuristic rules that can individually assess different aspects of data quality (completeness, consistency, validity). Each heuristic operates on specific data patterns, allowing the system to handle large volumes of data from multiple assets without compromising reliability. This segmentation enables parallel processing and modular error detection across diverse data sources.
Solution Approach 2:
The patent introduces an intermediary data validation layer between raw sensor data collection and predictive model execution. This intermediary process applies reconciliation heuristics to detect and correct data issues before they reach the analytics engine, acting as a buffer that maintains reliability regardless of the volume or variety of incoming data from industrial assets.
2Measurement precision
If data validation rules are made more stringent to detect inconsistencies, then data quality improves, but processing time increases
Solution Approach 1:
The patent implements a tiered validation approach where critical data fields undergo stringent validation while less critical fields receive lighter checking. The system applies reconciliation heuristics selectively based on data importance and anomaly detection needs, achieving high data quality for essential parameters without subjecting all data to maximum processing overhead, thus balancing quality and processing time.
Solution Approach 2:
Different validation strictness levels are applied to different data fields and assets based on their importance and reliability characteristics. Critical safety-related parameters receive exhaustive validation, while non-critical operational data receives streamlined checking. This local differentiation maintains high measurement precision where needed while reducing unnecessary processing time for less important data.
3Reliability
If manual review processes are used to verify data accuracy, then data reliability improves, but productivity decreases
Solution Approach 1:
The patent implements self-service automated validation where the system independently applies reconciliation heuristics to detect and correct its own data quality issues without human intervention. The automated error detection and correction mechanisms perform what would traditionally require manual review, maintaining high data reliability while preserving processing throughput by eliminating the need for human operators to verify each data point.
Solution Approach 2:
Manual review processes are replaced with automated computational validation algorithms that apply predefined reconciliation heuristics. The system substitutes human cognitive verification with machine-based pattern recognition and error detection, achieving comparable or superior reliability while dramatically increasing productivity by processing data at machine speeds without human intervention.
4Measurement precision
If complex reconciliation techniques are applied to correct data errors, then data accuracy improves, but device complexity increases
Solution Approach 1:
The complex reconciliation process is segmented into multiple simple, independent heuristic rules that each handle specific types of data errors. Rather than implementing one complex correction algorithm, the system applies a series of simple, targeted rules for different error conditions (missing values, inconsistent formats, invalid ranges), making the overall system more manageable and maintainable while achieving high data accuracy.
Solution Approach 2:
The patent transforms complex reconciliation logic into configurable parameters and rules that can be adjusted without changing the underlying system architecture. By parameterizing validation thresholds, error tolerances, and correction strategies, the system maintains high data accuracy while allowing flexibility in implementation complexity based on specific operational requirements.
Data Source
AI summary
Some aspects are directed to data reconciliation frameworks. An example framework is configured to receive core data, the system model comprising a plurality of data records associated with at least two assets, receive a system model, the system model comprising context data indicating, execute a configuration operation of a data validation process based on the system model, execute the data validation process to identify at least one inconsistent or incomplete record among the plurality of data records, determine at least one data reconciliation technique from a plurality of data reconciliation techniques based on the system model, and apply the at least one data reconciliation technique to the core data to reconcile the at least one inconsistent or incomplete record.