Industrial Boiler Fault Forewarning Using Segmented Parameter Images
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Solution Overview
Problem
Industrial boilers face challenges in detecting and predicting faults due to low thermal efficiency, poor safety, and high risk of undetected potential issues, as existing methods only detect faults after they occur and not before.
Innovation Solution
A method and intelligent system for recognizing and forewarning faults in industrial boilers using segmented and fragmented images of boiler monitoring parameters, which are input into a fault diagnosis model to predict and diagnose potential issues, incorporating models for slagging/scaling, burner faults, and drum thermal insulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time operation parameters are detected using sensors, then fault detection capability is provided, but only faults that have occurred can be detected and potential faults cannot be recognized
Solution Approach 1:
The patent applies preliminary action by segmenting the variation graph of boiler monitoring parameters into multiple fragmented images representing different time periods. These fragmented images capture the evolution of parameters over time, enabling the fault diagnosis model to identify potential faults before they manifest as actual failures. The segmentation allows the system to perform preliminary analysis of parameter trends and detect early signs of potential issues.
2Reliability
If segmented and fragmented images of boiler monitoring parameters are used, then potential faults can be recognized and predicted, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary fault diagnosis model that processes the segmented fragmented images of boiler monitoring parameters. This model acts as a mediator between the raw parameter data and fault detection, transforming complex time-series parameter variations into interpretable fault predictions. The intermediary model simplifies the overall system by centralizing the complex analysis function in a dedicated component rather than distributing complexity throughout the entire system.
3Measurement precision
If multiple boiler monitoring parameter combinations are analyzed, then fault detection accuracy is improved, but data processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the continuous variation graph of boiler monitoring parameters into multiple fragmented images, each representing a specific time period. This segmentation allows parallel processing of different parameter combinations across time periods, improving fault detection accuracy through comprehensive analysis while managing data processing time through structured organization of the analysis tasks.
Data Source
AI summary
The present disclosure provides a method and intelligent system for recognizing and forewarning a fault of an industrial boiler. The method for recognizing and forewarning the fault of the industrial boiler includes: acquiring preset boiler monitoring parameter combinations; acquiring a segmentation time span corresponding to each of the boiler monitoring parameter combinations; acquiring a variation graph of all the boiler monitoring parameters in each of the boiler monitoring parameter combinations, and segmenting and fragmenting the variation graph of all the boiler monitoring parameters in a time sequence according to the segmentation time span to obtain fragmented images; using the fragmented images of all the boiler monitoring parameters within the same time period in each of the boiler monitoring parameter combinations as a fragmented image combination to obtain a plurality of fragmented image combinations.