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A SOM-MQE-based health assessment method for ball screw pairs

A technology of health assessment and lead screw pair, applied in pattern recognition in signals, biological neural network models, instruments, etc., can solve problems such as lack of credibility

Active Publication Date: 2019-01-22
哈工大机器人(山东)智能装备研究院
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Problems solved by technology

[0005] Method 3: Research on life prediction of ball screw pair based on performance degradation model. By monitoring the current to obtain the axial force of the screw pair and knowing the rotational speed, the expected remaining life of the screw pair can be obtained, and the vibration signal is input together with the expected remaining life. To train the remaining life of the output screw in the dynamic fuzzy neural network, this method focuses on life prediction, and the remaining life is expected to be obtained through calculation, and the prediction results lack certain credibility

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  • A SOM-MQE-based health assessment method for ball screw pairs
  • A SOM-MQE-based health assessment method for ball screw pairs
  • A SOM-MQE-based health assessment method for ball screw pairs

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specific Embodiment approach 1

[0028] Specific implementation mode one: as figure 1 As shown, a SOM-MQE-based ball screw pair health assessment method described in this embodiment includes the following steps:

[0029] Step 1. Collect the vibration signals on the bearing supports at both ends of the lead screw sub-system and the nut. The vibration signals are collected through the three-channel vibration sensors installed on the bearing supports at both ends of the lead screw sub-system and the nut. figure 2 shown.

[0030] Step 2: Select the channel signal that best represents the degradation tendency of the corresponding component from the three channel signals output by each three-channel vibration sensor as the original signal for health assessment.

[0031] Step 3: The three preferred channel signals are preprocessed by wavelet noise reduction, and the processed signals are used as input for feature extraction.

[0032] Feature extraction is a key link in health assessment, and the quality of extrac...

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Abstract

A SOM-MQE-based health assessment method for ball screw pairs relates to a ball screw pair health evaluation technology. Firstly, the vibration signals of the bearing supports and the optimal channelson the screw thread are collected, then the signals are preprocessed and the features are extracted. Then the extracted eigenvalues are processed by SOM-MQE maps to health values; the alarm thresholdof health value curve is obtained by adaptive threshold detection algorithm. When the health value exceeds the alarm threshold, the alarm is automatically given, and the position of the problem is located on the specific components of the screw pair system. The method of the invention can evaluate the health state of the concrete parts in the screw pair, and provides the basis for the precise maintenance of the screw pair.

Description

technical field [0001] The invention relates to the health assessment technology of a ball screw pair. Background technique [0002] With the continuous improvement of the intelligent level of equipment and the rapid development of artificial intelligence technology, the intelligent maintenance of equipment has gradually become a research hotspot. Effective maintenance of equipment can ensure its safe and efficient operation. How to monitor and evaluate the health status of equipment when it is running, how to locate components with poor health status, and how to arrange maintenance are all problems that need to be solved urgently. As an important transmission component, ball screw is widely used in industries such as CNC machine tools, and its safe operation is crucial to the entire industrial equipment. Therefore, it is necessary to carry out health assessment on it. [0003] Method 1: The patent "A method for evaluating the health status of ball screw pairs" takes the ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06K9/00G06N3/04
CPCG06N3/04G06F30/17G06F2218/04G06F2218/08Y02P90/30
Inventor 单鹏飞于林明古乐李杨
Owner 哈工大机器人(山东)智能装备研究院
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