Dynamic Event Data Analysis System for Trend Identification
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Solution Overview
Problem
Current methods for analyzing event data, such as failure or performance data, often rely on assumptions of independence and identical distribution, which can be false in practice, leading to inadequate identification of trends and patterns, particularly in complex systems where internal and external factors are not fully understood or modeled.
Innovation Solution
A dynamic system and method for analyzing event data that allows users to create user-specified datasets, combine data elements, perform statistical trend analysis, and compute optimal inspection intervals, using linear, quadratic, and cubic polynomial fits, along with risk-based decision-making models, to identify improvement or deterioration trends and support maintenance strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard statistical analysis tools are used assuming independence and identical distribution, then analysis simplicity is maintained, but trend identification accuracy deteriorates
Solution Approach 1:
The system transitions from static assumption-based analysis to dynamic adaptive analysis. The analysis engine automatically adjusts analytical approaches based on data characteristics, eliminating the need for manual assumption verification while maintaining analysis simplicity. This resolves the contradiction by making the system adaptive rather than relying on fixed assumptions that may not hold in complex systems.
Solution Approach 2:
The system changes the fundamental parameters of statistical analysis by moving from independent identical distribution assumptions to models that account for dependencies, non-stationarity, and complex relationships. Multiple analytical methods are employed with varying degrees of complexity, allowing the system to achieve accurate trend identification without sacrificing operational simplicity through automation.
2Measurement precision
If complex analytical models are used to account for internal and external factors, then trend identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into multiple independent analytical engines, each handling specific types of patterns (trend analysis, pattern recognition, causality detection, prediction). This modular architecture allows complex functionality to be achieved while maintaining manageable system complexity through clear separation of concerns and specialized processing for different analytical needs.
Solution Approach 2:
The analysis system is designed as a universal platform that can handle multiple types of event data from diverse sources and domains. The same core infrastructure supports various analytical methods and data types, reducing overall system complexity by avoiding duplication across different analysis scenarios while maintaining high trend identification accuracy through methodological diversity.
3Measurement precision
If manual data specification and filtering is performed, then analysis precision is improved, but time consumption increases
Solution Approach 1:
The system implements self-service through automated data specification and filtering capabilities. The analysis engine automatically determines relevant data parameters, applies appropriate filtering criteria, and selects suitable analytical methods based on the characteristics of the input data. This eliminates manual intervention while maintaining high analysis precision through algorithmic decision-making that adapts to each dataset's unique properties.
Solution Approach 2:
The system performs preliminary actions by automatically preprocessing data, identifying patterns, and preparing datasets for analysis before formal processing begins. This includes automated data validation, transformation, and initial pattern recognition that reduces the time required for subsequent detailed analysis while ensuring high precision through thorough preliminary preparation.
Data Source
AI summary
Disclosed is a system and method for the analysis of event data that enables analysts to create user specified datasets in a dynamic fashion. Performance, equipment and system safety, reliability, and significant event analysis utilizes failure or performance data that are composed in part of time-based records. These data identify the temporal occurrence of performance changes that may necessitate scheduled or unscheduled intervention like maintenance events, trades, purchases, or other actions to take advantage of, mitigate or compensate for the observed changes. The criteria used to prompt a failure or performance record can range from complete loss of function to subtle changes in performance parameters that are known to be precursors of more severe events. These specific criteria applied to any explicit specific application and this invention is relevant to this type of data taxonomy and can be applied across all areas in which event data may be collected.