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Big data sensitive characteristic optimization selecting based equipment failure early warning method and system

A technology for sensitive features and equipment failures, applied in measuring devices, instruments, measuring ultrasonic/sonic/infrasonic waves, etc., and can solve problems such as low efficiency and low accuracy

Active Publication Date: 2019-07-05
ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Aiming at the technical problems of low accuracy and low efficiency of existing equipment early warning methods, the present invention proposes an equipment failure early warning method and system based on the optimal selection of sensitive features of big data, which improves the accuracy of big data early warning methods and can be implemented in real time. early warning

Method used

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  • Big data sensitive characteristic optimization selecting based equipment failure early warning method and system
  • Big data sensitive characteristic optimization selecting based equipment failure early warning method and system
  • Big data sensitive characteristic optimization selecting based equipment failure early warning method and system

Examples

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Comparison scheme
Effect test

Embodiment 1

[0066] Such as figure 1 As shown, a device failure early warning method based on the optimal selection of sensitive features of big data, the steps are as follows:

[0067] Step 1: Collect vibration data, process data and electrical data under normal working conditions of the equipment.

[0068] Vibration data refers to the data contained in the vibration signal generated when the device is working. Process data such as temperature, pressure and flow data; electrical data such as current and voltage data.

[0069] Step 2: Select the vibration data in Step 1, and perform feature extraction on it to obtain 15 feature quantities.

[0070] The 15 feature quantities obtained from the feature extraction of the vibration data in the step 2 and step 4 include peak value, peak-to-peak value, average amplitude, square root amplitude, effective value, 1-octave amplitude, and 2-octave amplitude , 3-octave amplitude, waveform index, pulse index, peak index, margin index, skewness index,...

Embodiment 2

[0125] Such as image 3 As shown, an equipment failure early warning system based on the optimal selection of sensitive features of big data, including: data acquisition module, feature quantity extraction module, early warning model training module and early warning module, data collection module and feature quantity extraction module and early warning model training module respectively The modules are connected, the feature quantity extraction module is connected with the early warning model training module, and the early warning model training module is connected with the early warning module.

[0126] The data acquisition module is used to collect vibration parameters, process parameters and electrical parameters of the equipment unit;

[0127] The feature quantity extraction module is used for feature extraction of vibration parameters under normal working conditions and test working conditions of the equipment;

[0128] The early warning model training module is used to...

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Abstract

The invention provides a big data sensitive characteristic optimization selecting based equipment failure early warning method and system. The method includes the following steps: collecting vibrationdata of equipment under a normal working condition, and performing time and frequency domain index feature extraction on the vibration data to form vibration-like feature vectors; applying a compensation distance assessment technique to perform optimization selecting on the vibration-like feature vectors, and commonly forming a sensitive vector set by the optimally selected vibration-like featurevectors and the process data of the equipment under the normal working condition to be a training sample which supports a vector data description model so that a SVDD hypersphere model of the equipment under the normal working condition can be formed through training; and processing testing vibration data by adopting the above same steps, and forming a testing sensitive vector set with the obtained optimized selection feature vectors and the process data under a testing working condition to input to the SVDD hypersphere model under the normal working condition, and performing early warning analysis on output results when the equipment is abnormal or is about to be abnormal. Thus, the intelligent maintenance of the equipment can be realized.

Description

technical field [0001] The invention relates to the technical field of equipment failure early warning, in particular to an equipment failure early warning method and system based on optimal selection of sensitive features of big data. Background technique [0002] Equipment maintenance costs account for the majority of equipment management, and traditional equipment maintenance models have problems such as insufficient maintenance or excessive maintenance: the former may cause major accidents, while the latter will increase unnecessary maintenance costs. Realizing early warning of equipment can take corresponding measures in advance in the early stage of equipment failure to avoid major accidents, and change passive maintenance into active maintenance, thereby effectively reducing enterprise equipment management costs. [0003] Conventional early warning methods for equipment based on big data often use the original monitoring data of equipment or unoptimized extracted feat...

Claims

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Application Information

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IPC IPC(8): G01D21/02G01H17/00G06K9/62
CPCG01D21/02G01H17/00G06F18/24
Inventor 王宏超郭志强杜文辽巩晓赟
Owner ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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