Fault Signal Recovery Using Similarity-Based Model Selection
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Solution Overview
Problem
Current fault signal recovery technologies in early warning systems for plants are inefficient, leading to low accuracy in restoring fault signals to normal signals, which hampers the construction of accurate failure prediction models.
Innovation Solution
A fault signal recovery system that generates a signal subset by removing fault signals, uses a modeling unit to extract feature information and recovery information from learning signal subsets, and employs a recovery unit to estimate normal signals based on similarity parameters, combining recovery algorithms to optimize the recovery process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fault signals are removed from the signal set to create a clean learning dataset, then the quality of learning data is improved, but the quantity of available data decreases
Solution Approach 1:
The patent recovers fault signals and transforms them into beneficial learning data. Instead of discarding fault signals as harmful data, the system uses recovery algorithms to reconstruct the original normal signals from fault-containing signals, converting the harmful fault data into useful learning data for training failure prediction models.
Solution Approach 2:
The patent changes the state of fault signals by applying recovery algorithms that transform fault-containing signals into recovered normal signals. This parameter transformation allows the same data to serve dual purposes: maintaining data quantity while improving data quality through signal reconstruction.
2Ease of manufacture
If traditional fault signal recovery methods are used, then the recovery process is simple, but the accuracy of signal recovery is low
Solution Approach 1:
The patent segments the recovery process into multiple independent recovery algorithms that can be applied to different signal characteristics. By dividing the recovery task into multiple specialized algorithms (e.g., different recovery models for different types of faults), the system achieves higher accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent combines multiple recovery algorithms into an integrated recovery system that selects and applies the most appropriate algorithm based on the characteristics of the input signal. This merging of multiple approaches enables the system to achieve high accuracy across diverse fault types while presenting a unified, easy-to-use interface.
Data Source
AI summary
Disclosed is a fault signal recovery system including a data processor configured to generate a signal subset U* by removing, from a signal set U for a plurality of tags, some tags including a fault signal, and a first learning signal subset X* by removing tags disposed at positions corresponding to the some tags from a learning signal set X containing only tags of normal signals, a modeling unit configured to generate feature information F extractable from the first learning signal subset X* and recovery information P on a plurality of recovery models usable for restoring the fault signal, and a recovery unit configured to estimate and recover normal signals for the some tags based on the signal subset U*, the first learning signal subset X*, the feature information F, the recovery information P on the plurality of recovery models, and similarity Z.