Early Warning of Abnormal Operating Modes via Feature Extraction from Cross-Section Frame at Discharge End for Sintering Process

TL;DR

This paper presents a new method to detect abnormal sintering process modes early by analyzing images and process data, improving production stability and quality.

Contribution

A novel early warning system integrating image analysis and Bayesian neural networks for detecting sintering process anomalies.

Findings

The method achieved 94.07% accuracy in detecting abnormal operating modes.

Combining visual features and process variables improves early warning performance.

Abstract

Abnormal operating modes in the iron ore sintering process often lead to reduced productivity and inferior sinter quality. The timely early warning of such modes is therefore essential in maintaining stable production and ensuring product quality. To this end, we develop an early warning approach that integrates cross-sectional image features from the discharge end. First, an edge detection-based scheme is designed to isolate and analyze the red fire layer in the image. Second, a random forest feature importance ranking is employed to select process variables. Third, a Bayesian neural network is trained on both selected process variables and visual features extracted from the red fire layer to construct the early warning model. Finally, the burn-through point is adopted as the classification criterion, and experiments are carried out on raw data collected from an industrial plant. The results demonstrate that the proposed method enables the accurate early detection of abnormal operating modes, achieving accuracy of 94.07%, and thus holds strong potential for industrial application.

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