Drift fault recognition method of hot-rolling strip steel based on sound signals

A technology for hot-rolled strip steel and fault identification, applied in metal rolling, metal rolling, length measuring devices, etc., can solve problems such as lack of online detection means, high noise, and complexity

Active Publication Date: 2012-06-27
UNIV OF SCI & TECH BEIJING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the complex production environment on site, the noise is extremely high, and the weak flick sound is easily submerged in the noise. At present, there is no effective online detection method for the flick phenomenon of hot-rolled strip steel at home and abroad, especially the strip flick by acoustic methods. Detection is a pioneering research work at home and abroad

Method used

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  • Drift fault recognition method of hot-rolling strip steel based on sound signals
  • Drift fault recognition method of hot-rolling strip steel based on sound signals
  • Drift fault recognition method of hot-rolling strip steel based on sound signals

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0105] The original signal is resampled at a frequency of 11kHz with a frame length of 1000 data points and a frame shift of 340 data points. The selection of each parameter is listed in Table 1, from which it can be obtained that the recognition rate of tail-flick fault is 97.47%, and the recognition rate of normal state is 97%.

[0106] Selection of each parameter in Table 1

[0107] Pivot number k

Embodiment approach 2

[0109] The original signal is resampled at a frequency of 11kHz with a frame length of 1000 data points and a frame shift of 340 data points. The selection of each parameter is listed in Table 2. From this, the recognition rate of tail-flick fault is 96.2%, and the recognition rate of normal state is 96%.

[0110] Selection of each parameter in Table 2

[0111] Pivot number k

Embodiment approach 3

[0113] The original signal is resampled at a frequency of 11kHz with a frame length of 1000 data points and a frame shift of 340 data points. The selection of each parameter is listed in Table 3. From this, it can be obtained that the recognition rate of tail-flick fault is 98.73%, and the recognition rate of normal state is 95%.

[0114] Selection of each parameter in table 3

[0115] Pivot number k

Confidence test level a

Distribution value F

Total overrun rate ρ

18

0.001

2.336

0.06

[0116] Summing up the above description, the present invention comprises the following steps:

[0117] 1) Perform resampling and frame preprocessing on the input signal.

[0118] 2) Using Mel frequency cepstrum technology to extract the features of strip flick signal.

[0119] 3) Using principal component analysis and multivariate statistical process control charts for feature selection, a tail-flick recognition model was established.

[0...

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Abstract

The invention provides a drift fault recognition method of hot-rolling strip steel based on sound signals. The method comprises the following steps of: preprocessing a signal by combining the features of a hot-rolling production line and adopting a resampling and framing technology, carrying out feature extraction on the signal by adopting a Mel frequency cepstrum technology and carrying out feature selection and recognition by adopting a principal component analysis method and a multivariate statistic process T2 control diagram so as to realize the diagnosis of drift faults of the hot-rolling strip steel. The invention has the advantages that the drift phenomenon can be judged accurately and rapidly on line by utilizing an acoustic detection method, the roller replacement time can be reasonably arranged, the roller consumption can be effectively reduced, the production cost can be controlled, and the quality defect prevention capacity and the production operability of the product canbe improved.

Description

technical field [0001] The invention relates to a method for identifying tailing faults of hot-rolled strip steel by using acoustic signals. Hot-rolled strip tailing is an abnormal phenomenon in production. On-line monitoring of the abnormal sound emitted by strip steel tailing, and extraction of acoustic signal features for tailing fault identification is a novel and effective method for tailing fault identification. Background technique [0002] Hot-rolled strip tailing is an abnormal production phenomenon caused by strip shape control, improper loop control, and uneven temperature distribution along the width direction of the strip. It generally occurs in thin gauge, hard material, wide rolled pieces and silicon steel. Waiting for production. In the production of hot-rolled strip steel, the phenomenon of tail flicking, especially if it occurs continuously, will cause the tail of the strip to be folded and broken, which will cause great harm to the production. If the bro...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B21B38/00
Inventor 阳建宏黎敏李雪瑞陈刚康新成陈翔魏立泽周战郑耀中孟传胜
Owner UNIV OF SCI & TECH BEIJING
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