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A signal segmentation method for real-time monitoring of drilling processing status

A processing status and real-time monitoring technology, applied in metal processing equipment, metal processing machinery parts, manufacturing tools, etc., can solve problems such as real-time impact and detection failure, so as to improve reliability, improve real-time performance, and shorten analysis and processing time Effect

Active Publication Date: 2019-12-31
XIANGTAN UNIV
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Problems solved by technology

Then, the signal data collected in the state monitoring process will be massive, and if all of them are processed, it will undoubtedly affect the real-time performance of the monitoring, and even the detection will fail.

Method used

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  • A signal segmentation method for real-time monitoring of drilling processing status
  • A signal segmentation method for real-time monitoring of drilling processing status
  • A signal segmentation method for real-time monitoring of drilling processing status

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Embodiment 1

[0024] Embodiment 1, it mainly realizes the automatic detection of actual cutting (elimination of empty cutting) to select a stable signal segment representing the processing state and solve the problem of too much data in the signal processing process. The key points of the technical solution are: firstly, the wavelet packet decomposition is used for the drilling monitoring signal, and the normalized Shannon energy of each layer signal is calculated respectively; then, the Shannon envelope of the layer signal with the largest Shannon energy is reconstructed as the drilling monitoring signal Envelope; then, use the moving average algorithm to smooth the signal envelope, and realize the dual-threshold adaptive signal detection and segmentation by continuously updating the noise signal estimation in real time; finally, calculate the theoretical length of the cutting signal segment according to the drilling processing parameters, and introduce the theoretical length into the detect...

Embodiment 2

[0025] Embodiment 2, the wavelet packet decomposition is to decompose the drilling monitoring signal X(t) using db5 wavelet 3 layers, and decompose the signal into 8 different frequency bands: s130, s131,..., s137, wavelet basis function and decomposition layer The number is determined by comparing the signal segmentation effect through experiments. refer to Figure 1 to Figure 8 , all the other are with embodiment 1.

Embodiment 3

[0026] Embodiment 3, the normalized Shannon energy of each layer signal after the calculation is decomposed, and the Shannon envelope of the layer signal with the largest Shannon energy is reconstructed as the drilling monitoring signal envelope. The calculation process is: first, for s130, s131 ,...,s137 for normalization x(i)=s13i norm =s13i / max(|s13i|); Then, calculate its Shannon energy E(i)=-x for the normalized signal 2 (i)log[x 2 (i)]; finally, obtain the Shannon envelope p(i)=[E(i)-M(E(i))] / S(E(i)) of the signal, M(E(i)) is Shannon energy mean, S(E(i)) Shannon energy variance. refer to Figure 1 to Figure 8 , and the rest are the same as the above-mentioned embodiment.

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Abstract

Provided is a signal division method for real-time monitoring of the drilling machining state. The method is mainly used for achieving full-automatic actual cutting (eliminating empty cutting) detection to select stable signal sections representing the machining state and solving the problem that too much data exist in the signal handling process. According to the technical scheme, the signal division method is characterized in that firstly, drilling machining monitoring signals are subjected to wavelet packet decomposition, and the normalized Shannon energy of each layer of signals is calculated; then, a Shannon envelope of the signals of the layer with the maximum Shannon energy is reconfigured as a drilling monitoring signal envelope; then the signal envelope is smoothened through a sliding average algorithm, and dual threshold adaptive signal detection division is achieved by continuously updating noise signal estimation in real time; and finally, the theoretical length of cuttingsignals is calculated according to drilling machining parameters, the length is introduced in the detection process to serve as a judgment extra constraint condition to correct a result, and thus accurate separation of the actual cutting signal from the empty cutting signal in the drilling process is completed. The method can be widely applied to machining state real-time online monitoring systems.

Description

technical field [0001] The invention relates to a signal processing method for drilling state monitoring, in particular to the field of continuous online real-time state monitoring for a long time. Background technique [0002] With the proposal of the "Made in China 2025" manufacturing power strategy, the government, enterprises and universities are closely focusing on key links in key manufacturing fields, and are actively developing integrated innovation and engineering applications of the integration of new generation information technology and manufacturing equipment. As an important part of predictive maintenance, machining condition monitoring plays an important role in ensuring product processing quality and reducing enterprise losses. At the same time, the real-time performance of condition monitoring is an important evaluation index to measure the performance of condition monitoring system. [0003] Considering cost and other reasons, most of the detection of equip...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B23Q17/09
CPCB23Q17/0952
Inventor 周友行李勇赵晗妘徐志刚
Owner XIANGTAN UNIV
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