Early Event Detection Using PCA and Fuzzy Logic Models
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Solution Overview
Problem
Complex systems and processes in various fields face challenges in predicting and detecting early events due to the complexity of inter-related parameters, which existing predictive models struggle to address effectively.
Innovation Solution
A system and method for early event detection involving the generation of multiple models, normalization of output signals, identification and estimation of invalid data, and graphical displays to enhance predictive capabilities, utilizing techniques like rank revealing QR factorization and Fuzzy Logic models for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple predictive models are used to improve prediction accuracy for complex systems, then the reliability of event detection is improved, but the device complexity increases
Solution Approach 1:
The system segments the prediction task by using multiple specialized models (first model for initial predictions, second model for adjustments, third model for validation) rather than one complex model. Each model handles a specific aspect of the prediction process, improving overall reliability while managing complexity through functional division.
Solution Approach 2:
The prediction process is nested with multiple levels of modeling: the first model generates initial predictions, the second model adjusts these predictions, and the third model validates the final results. This nested structure allows each model to build upon the previous one, enhancing accuracy through layered analysis.
2Measurement precision
If comprehensive operating data is collected to improve prediction accuracy, then the measurement precision is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system extracts and removes invalid data elements from the comprehensive operating data before processing. By identifying and eliminating corrupt or erroneous data points, the system maintains high measurement precision while reducing the complexity of data processing through selective extraction of valid information.
Solution Approach 2:
The system performs preliminary data validation and cleaning operations before the main prediction process. Invalid data elements are identified and removed in advance, ensuring that only high-quality data enters the modeling stage, thereby improving measurement precision while managing processing complexity through upfront data preparation.
3Reliability
If model training uses all available data to improve predictive accuracy, then the reliability is improved, but the loss of time in data processing increases
Solution Approach 1:
The system uses partial action by training models on selected subsets of data rather than all available data. The first model is trained on initial data subsets, and subsequent models are trained on progressively refined data sets, achieving sufficient accuracy without the time cost of processing every available data point.
Solution Approach 2:
Data preprocessing and validation are performed in advance to prepare training data before model training begins. This preliminary action ensures that models are trained on clean, validated data subsets, improving reliability while reducing training time by avoiding reprocessing of obviously invalid or redundant data during the training phase.
Data Source
AI summary
Various methods, devices, systems, and computer programs are disclosed relating to the use of models to represent systems and processes (such as manufacturing and production plants). For example, a method may include generating a first model and a second model using operating data associated with a system or process. The method may also include using the first and second models to predict one or more events associated with the system or process. The one or more events are predicted by generating one or more initial event predictions using the first model and adjusting the one or more initial event predictions using the second model. The first model may represent a Principal Component Analysis (PCA) model, and the second model may represent a Fuzzy Logic model.