Tool condition monitoring and identifying method based on signal fusion and multi-fractal spectrum algorithm

A multi-fractal spectrum and signal fusion technology, which is applied in the field of tool condition monitoring and recognition based on signal fusion and multi-fractal spectrum algorithm, can solve the problems of inability to accurately establish signals and tool wear, and achieve good signal characteristics and high recognition effects.

Inactive Publication Date: 2020-01-07
SHANDONG UNIV
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Time-domain analysis and frequency-domain analysis are the most commonly used analysis methods for signal analysis. The information of the signal is analyzed from two different angles. The inventors found that the extracted features are mostly based on the feature quantities obtained from sta

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  • Tool condition monitoring and identifying method based on signal fusion and multi-fractal spectrum algorithm
  • Tool condition monitoring and identifying method based on signal fusion and multi-fractal spectrum algorithm
  • Tool condition monitoring and identifying method based on signal fusion and multi-fractal spectrum algorithm

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[0070] It should be pointed out that the following detailed descriptions are all illustrative and are intended to provide further explanations for the application. Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0071] It should be noted that the terms used here are only for describing specific implementations, and are not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate There are features, steps, operations, devices, components, and / or combinations thereof.

[0072] For the convenience of description, if the words "up", "down...

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Abstract

The invention relates to a tool condition monitoring and identifying method based on a signal fusion and multi-fractal spectrum algorithm. The tool condition monitoring and identifying method based onthe signal fusion and multi-fractal spectrum algorithm comprises the following steps of (1) in the cutting process, acquiring a cutting force signal and a vibrating signal; (2) denoising the cuttingforce signal and the vibrating signal acquired in the step 1; (3) for a denoised signal sequence, analyzing multi-fractal characteristics of the signals, searching the relation between the signals andthe tool wear through the multi-fractal characteristics, extracting relevant characteristic vectors from a multi-fractal spectrum obtained through calculation, and representing the relation between the signals and the tool wear through the characteristic vectors; and (4) merging the characteristic vectors extracted in the step 3 into a characteristic matrix, using as an input parameter variation,building a support vector machine model for tool wear condition monitoring, and utilizing the optimized support vector machine model for diagnosing the unknown tool condition. By adopting the methodprovided by the invention, the high identification rate of the tool condition is achieved.

Description

Technical field [0001] The invention relates to the technical field of mechanical processing and advanced manufacturing, in particular to a tool condition monitoring and recognizing method based on signal fusion and multifractal spectrum algorithm. Background technique [0002] Tool status intelligent monitoring technology is a fusion of technologies including sensor application, digital signal recognition and analysis, computer programming, and artificial intelligence machine learning. It has a significant role in promoting the automation of mechanical processing and the unmanned manufacturing process. It is used in intelligent manufacturing. The field occupies an increasingly important position. [0003] The current intelligent monitoring of tool status mostly uses a single signal for monitoring, including cutting force signals, vibration signals, acoustic emission signals and other signals. Although single-signal monitoring has the advantages of simple and easy operation, it ha...

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Application Information

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IPC IPC(8): B23Q17/09
CPCB23Q17/0957B23Q17/0966B23Q17/0971
Inventor 李安海郭景超
Owner SHANDONG UNIV
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