Cutter damage adaptive alarm method based on wavelet packet and probability neural network

A probabilistic neural network and self-adaptive technology, which is applied in the field of self-adaptive alarming of tool damage based on wavelet packet analysis and probabilistic neural network modeling, can solve the problems of prone to missed and false positives, achieve real-time detection of tool status, improve The effect of precision

Inactive Publication Date: 2011-09-07
XI AN JIAOTONG UNIV
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  • Cutter damage adaptive alarm method based on wavelet packet and probability neural network
  • Cutter damage adaptive alarm method based on wavelet packet and probability neural network

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[0014] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0015] With reference to accompanying drawing, based on wavelet packet and probabilistic neural network tool breakage self-adaptive warning method, comprises the following steps:

[0016] The first step is to polish the part of the tool bar where the acoustic emission sensor is fixed, apply butter, and then fix the acoustic emission sensor to the tool bar, and use the acoustic emission signal data acquisition program based on Labview to collect the acoustic emission signal through the PCI card;

[0017] In the second step, the collected acoustic emission signals are analyzed by three-layer wavelet packets, and each group of signals is decomposed into eight frequency bands, namely 0~124khz, 125~249khz, 250~499khz, 500~549khz, 550~599khz, 600~649khz, 650~699khz, 700~749khz, among them, 125~249khz and 250~499khz are the two frequency bands with the largest energ...

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Abstract

The invention discloses a cutter damage adaptive alarm method based on a wavelet packet and a probability neural network. The method comprises the following steps of: fixing an acoustic emission sensor on a cutter bar, acquiring acoustic emission signals, performing three-layer wavelet packet analysis, selecting characteristic frequency bands and taking root mean square values thereof, normalizing the root mean square values to obtain smoothing factors and prior probability, establishing a cutter damage state probability model by using a probability neural network, determining an alarm value of the cutter abrasion state according to the model and the Pauta criterion, forming a dynamic alarm line, and performing adaptive alarm monitoring of the cutter operating state according to the dynamic alarm line. By the method, the probability distribution curve of the root mean square value related with the cutter abrasion can be found, the alarm value is determined by using a mathematical statistic method, the dynamic alarm line is formed together with the cutter abrasion state change, and missing alarm and error alarm are not caused.

Description

technical field [0001] The invention relates to the field of machine tool tool state monitoring, in particular to an adaptive alarm method for tool damage based on wavelet packet analysis and probabilistic neural network modeling. Background technique [0002] Adaptive alarm technology means that the alarm indicators should change with the actual conditions of the equipment, such as working conditions, working hours, power, speed, etc., and its goal is to establish a dynamic evaluation rule between the alarm indicators and the operation of the equipment to form a changing dynamic alarm. curve. [0003] Due to the complexity and diversity of the manufacturing process, the life of the tool is discretely distributed, causing many tools to be replaced in advance or later, resulting in unnecessary waste of tools. Due to processing quality problems, tool status detection is necessary. The current equipment status alarm technology is still based on static alarm. Once the parameter...

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Application Information

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IPC IPC(8): B23Q17/09B23Q11/00
Inventor 徐光华姜阔胜张庆孟理华
Owner XI AN JIAOTONG UNIV
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