Generator EMI Baseline Libraries for Mode-Specific Abnormality Detection
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Solution Overview
Problem
Existing methods lack a quantitative approach to identify normality in electromagnetic interference (EMI) signatures of generators, complicating the detection of abnormalities due to each generator having a unique signature without a universal baseline.
Innovation Solution
A method is developed to create a baseline library using historical EMI signature data and operational modes of generators, establishing a normality zone through statistical analysis, allowing for the classification of abnormalities using an abnormality score based on frequency ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a universal baseline is used for all generators, then the system complexity is reduced, but the measurement precision deteriorates because each generator has a unique signature
Solution Approach 1:
The patent segments the baseline approach by creating individual baseline libraries for each generator rather than using a single universal baseline. This allows each generator's unique EMI signature characteristics to be captured and stored separately, enabling precise abnormality detection while maintaining manageable system complexity through automated data collection and processing
Solution Approach 2:
The patent applies preliminary action by collecting and storing historical EMI data and operational mode information before abnormality detection occurs. This historical data is processed to establish baseline characteristics for each generator, which are then used to define normality zones that enable accurate real-time abnormality detection
2Measurement precision
If individual baseline libraries are created for each generator, then the measurement precision improves, but the device complexity increases due to data collection and processing requirements
Solution Approach 1:
The system performs self-service by automatically collecting, storing, and processing historical EMI data and operational mode information to create baseline libraries without requiring manual intervention. The automated processes handle data collection from sensors, data processing, baseline generation, and abnormality detection, reducing the operational burden while maintaining high measurement precision
Solution Approach 2:
The patent implements feedback mechanisms where historical EMI data and operational mode information are continuously collected and processed to refine baseline libraries. This feedback loop allows the system to adapt to changing generator characteristics and maintain accurate abnormality detection precision while managing data processing complexity through iterative improvement
3Reliability
If historical EMI data is collected and processed to create baselines, then the reliability of abnormality detection improves, but the loss of time increases due to data collection and processing requirements
Solution Approach 1:
The patent applies preliminary action by collecting and processing historical EMI data and operational mode information in advance to establish baseline libraries before abnormality detection is needed. This pre-processing of data enables rapid and reliable abnormality detection in real-time operations without requiring time-consuming data collection and processing during critical moments
Solution Approach 2:
The system maintains continuity of useful action by continuously collecting and processing EMI data and operational mode information to update baseline libraries. This continuous process ensures that the most current and relevant data is always available for reliable abnormality detection, balancing the time required for data processing with the need for high reliability
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces false alarms by providing a suitable baseline for generator operations, enabling precise identification of electrical defects and facilitating targeted maintenance by correlating EMI patterns with operational modes.
Implementation Method 1
receiving a measurement of electromagnetic interference (EMI) data from the at least one radio frequency current transformer
Data Source
AI summary
A system and method of monitoring equipment performance and predicting failures and required maintenance. One aspect of the present invention uses historical generator electro magnet interference (EMI) signature data and their corresponding generator operational modes (out-of-service and active power output) to generate a baseline library for each generator in the fleet. In this library, each baseline signature is the statistical leverage of all the historical EMI signatures when the generator outputs a certain amount of active power and when the generator is out-of-service. A normality zone associated with each baseline is also provided using the statistical distribution of each data point on the signature curve. So that engineers can use the most suitable baseline when identifying abnormality given certain generator output. The correspondence between EMI signature patterns and generator operational modes is also proved and demonstrated using real-world data.