Rolling Mill Vibration Mapping for Accurate Chatter Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for detecting abnormal vibration in rolling mills often result in erroneous detections due to noise and difficulty in identifying chatter marks caused by minute vibrations, especially in continuous cold rolling mills where varying line speeds and superimposed frequencies complicate the detection of chatter marks.
Innovation Solution
A method involving vibration data collection, frequency analysis, data conversion to standard intervals based on rolling speed, and principal component analysis to generate vibration and outlier component maps, allowing for accurate detection of abnormal vibrations by visualizing changes in vibration intensity over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vibration detection methods are used in rolling mills, then vibration data can be obtained, but erroneous detection occurs due to noise from equipment around the rolling mill and vibration from other sources
Solution Approach 1:
The patent segments the vibration detection system into multiple vibration detectors positioned at different locations (rolling mill body, rolls between stands, entry and exit sides). Each detector captures vibration from specific sources, allowing the system to distinguish between noise from equipment around the rolling mill and actual chattering vibration by analyzing the spatial distribution and characteristics of detected vibrations.
Solution Approach 2:
The patent introduces frequency analysis as an intermediary process that processes raw vibration data from multiple detectors. By performing frequency analysis and comparing the results with predetermined frequency ranges for chattering, the system mediates between the complex vibration signals and the detection decision, effectively filtering out noise from other vibration sources.
2Measurement precision
If frequency analysis is performed on vibration data from multiple locations, then comprehensive vibration monitoring is achieved, but it becomes difficult to identify the specific frequency at which chattering occurs due to superimposed vibrations of plural frequencies
Solution Approach 1:
The patent implements a feedback mechanism where the vibration detection system continuously monitors vibration frequencies and compares them with predetermined frequency ranges specific to chattering. When vibration frequencies fall within these ranges, the system provides feedback indicating chattering occurrence. This feedback loop allows the system to distinguish chattering frequencies from other superimposed vibrations by continuously referencing the characteristic frequency signature of chattering.
Solution Approach 2:
The patent changes the parameter of frequency analysis by performing frequency analysis on vibration data from multiple detectors and comparing the results with predetermined frequency ranges. Instead of analyzing a single frequency value, the system analyzes the distribution of vibration frequencies across multiple detection points and identifies chattering by detecting the specific frequency pattern that matches the predetermined chattering frequency range, thereby distinguishing it from other superimposed vibrations.
3Measurement precision
If vibration detectors are attached to multiple locations including rolls and around the rolling mill, then complete vibration coverage is achieved, but device complexity increases
Solution Approach 1:
The patent applies universality by using identical vibration detector devices at multiple locations (rolling mill body, rolls between stands, entry and exit sides). Each detector performs the same function of capturing vibration data, and all detectors are processed through a unified frequency analysis system. This multi-functional approach allows comprehensive vibration coverage while maintaining consistency in the detection methodology, reducing the need for location-specific customization and simplifying system management.
4Ease of operation
If conventional detection methods are used, then simple detection can be performed, but it is difficult to detect chattering with high accuracy in continuous cold rolling mills where line speed varies from one stand to another
Solution Approach 1:
The patent applies preliminary action by establishing predetermined frequency ranges for chattering before the actual detection process. These frequency ranges are determined based on the characteristic frequencies of chattering in continuous cold rolling mills. By having these reference ranges prepared in advance, the system can quickly compare real-time vibration data against known chattering signatures, enabling accurate detection even when line speed varies between stands, without requiring complex real-time calculations or adjustments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively prevents erroneous detection and allows for high-accuracy identification of abnormal vibrations, thereby preventing surface defects in metal strips and ensuring consistent production quality.
Implementation Method 1
a vibration detector attached to each of the rolling mills performs frequency analysis on vibration values obtained at a predetermined timing interval
Data Source
AI summary
A method for detecting abnormal vibration of a rolling mill including a collecting step of collecting vibration data of the rolling mill, a frequency analysis step of generating first analysis data indicative of vibration intensities at respective frequencies by performing frequency analysis of the vibration data, a data conversion step of converting the first analysis data into second analysis data indicative of vibration intensities at respective intervals on the basis of a rolling speed, and a map generation step of generating a vibration map in which a plurality of the second analysis data are arranged in time series.