Crank Flying Shear Monitoring via Cutting Speed Stage Segmentation
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Solution Overview
Problem
Crank flying shear processes in hot-rolling production lines face challenges due to unstable operating conditions and high impact loads, leading to ineffective monitoring and diagnosis, resulting in economic losses and safety risks, as existing methods based on voltage and temperature are inadequate.
Innovation Solution
A monitoring method that acquires control signals and actual cutting edge speed curves to divide the cutting process into sub-stages, combining sensor data to detect abnormalities, using clustering analysis and abnormality detection models to estimate risk and determine fault causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing monitoring methods based on voltage and temperature are used, then the monitoring system is simple to implement, but they are unable to effectively monitor and diagnose crank flying shear processes due to unstable operating conditions and high impact loads
Solution Approach 1:
The cutting process is divided into multiple sub-processes (acceleration sub-process, cutting sub-process, deceleration sub-process) based on the cutting edge speed curve. This segmentation allows for targeted monitoring and diagnosis of specific process stages, improving reliability without requiring a completely complex system overhaul.
Solution Approach 2:
The monitoring system dynamically adapts to unstable operating conditions by using real-time cutting edge speed curves to identify and monitor different sub-processes. The system adjusts its monitoring focus based on the current operational phase, making it effective under varying conditions without requiring excessive complexity.
2Reliability
If manual spot checks and excessive preventive maintenance are employed, then fault detection can be performed, but unplanned shutdowns still occur and economic losses are incurred
Solution Approach 1:
The system establishes a feedback mechanism by comparing actual process data with typical data for each sub-process. This enables real-time detection of abnormalities and early warning of potential faults, allowing for timely intervention before unplanned shutdowns occur, thus maintaining production continuity.
Solution Approach 2:
The monitoring system performs preliminary detection and diagnosis of potential faults by analyzing process data trends before actual failures occur. This preliminary action enables preventive maintenance to be scheduled at optimal times, avoiding unplanned shutdowns while reducing excessive maintenance.
3Measurement precision
If the cutting process is monitored as a whole without subdivision, then the monitoring approach is simple, but abnormality detection precision is reduced due to the complex and varying characteristics of different cutting stages
Solution Approach 1:
The cutting process is segmented into distinct sub-processes (acceleration, cutting, deceleration) based on the cutting edge speed curve. Each sub-process has characteristic typical data that can be used for precise comparison and abnormality detection, significantly improving measurement precision.
Solution Approach 2:
Different monitoring criteria and typical data are applied to different sub-processes according to their specific characteristics. The acceleration sub-process uses different reference data than the cutting sub-process, which in turn uses different data than the deceleration sub-process, enabling precise localised detection.
Data Source
AI summary
The present invention provides a monitoring method for a plate billet crank flying shear process, including: acquiring a control signal of a control system of a crank flying shear device, and determining a cutting stage of the crank flying shear process according to the control signal; obtaining an actual cutting edge speed curve in the cutting stage, and further dividing the cutting stage into multiple sub-processes according to the actual cutting edge speed curve; obtaining actual data of a parameter related to the crank flying shear process, and for one or more of the multiple sub-processes, separately comparing the actual data of the parameter with typical data of the parameter, in order to estimate an abnormality risk.