Machine tool cutter vibration monitoring and analyzing method based on artificial intelligence and big data

A technology of artificial intelligence and analysis methods, applied in manufacturing tools, neural learning methods, electrical digital data processing, etc., can solve the problems of poor versatility and applicability, high signal-to-noise ratio, and low precision of characteristic signal processing, and achieve installation and deployment Convenience and simple feature extraction

Pending Publication Date: 2020-10-16
GTCOM TECH QINGDAO CO LTD
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Problems solved by technology

[0003] Traditional key component monitoring and analysis technologies are based on acoustic emission, current, power, torque and other technologies. Generally speaking, the sensors used are large in size, expensive, low in accuracy, limited in installation, and interfered in processing.
The extracted feature signals often have

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  • Machine tool cutter vibration monitoring and analyzing method based on artificial intelligence and big data
  • Machine tool cutter vibration monitoring and analyzing method based on artificial intelligence and big data
  • Machine tool cutter vibration monitoring and analyzing method based on artificial intelligence and big data

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Embodiment Construction

[0026] The present invention is specifically described below in conjunction with accompanying drawing, as Figure 1-5 As shown, in this technical solution, such as figure 1 As shown, in the offline mode, the original vibration signal collected in the experiment is pre-processed first to obtain training samples, and then the training samples are input into the neural network for training, and the trained neural network weights are serialized to In the hard disk, avoid the need to repeatedly train the neural network. In the online model mode, the neural network weight file that has been trained and serialized to the hard disk is loaded into the neural network, and then the real-time vibration signal collected by the sensor is pre-processed to obtain the sample to be recognized, and the sample to be recognized is obtained. The samples are input into the neural network, and the predicted value of tool wear can be obtained.

[0027] The data acquisition system consists of a vibra...

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Abstract

The invention discloses a machine tool cutter vibration monitoring and analyzing method based on artificial intelligence and big data. According to the method, on the premise of big data, artificial intelligence methods such as machine learning and deep learning are combined to perform feature extraction, feature transformation and feature learning on vibration signals generated by key devices soas to monitor and analyze the key devices. The beneficial effects of the invention are that the vibration analysis is simple in feature extraction, is convenient to install and deploy, and does not damage the structure of a measured object body.

Description

technical field [0001] The invention relates to the field, in particular to a method for monitoring and analyzing machine tool tool vibration based on artificial intelligence and big data. Background technique [0002] With the development of science and technology, problems caused by vibration and dynamic characteristics of equipment have attracted great attention from all walks of life. For example: the vibration of mechanical processing equipment directly affects the accuracy and effectiveness of processing; the vibration signal of large rotating machinery directly reflects the main information of equipment operation. Therefore, the analysis technology of vibration signal is one of the important branches of mechanical dynamics, and it is an extremely common aspect of mechanical dynamics engineering application. With the development of equipment toward automation and high speed, the problems caused by vibration are more prominent, and the problems that need to be solved a...

Claims

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Application Information

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IPC IPC(8): G06F16/182G06N3/04G06N3/08B23Q17/09
CPCG06N3/08G06F16/182B23Q17/0971G06N3/045
Inventor 张慧
Owner GTCOM TECH QINGDAO CO LTD
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