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Method and system for optimizing maintenance of mechanical system based on proportional hazard model

A mechanical system and failure rate technology, applied in design optimization/simulation, special data processing applications, instruments, etc., can solve problems such as immature monitoring methods, and achieve the effect of accurate maintenance or replacement time

Inactive Publication Date: 2017-10-13
HANGZHOU ANMAISHENG INTELLIGENT TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Aiming at the shortcomings of the existing monitoring methods in the prior art that are not mature enough, the present invention provides a maintenance method based on a proportional failure rate model to optimize the mechanical system

Method used

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  • Method and system for optimizing maintenance of mechanical system based on proportional hazard model
  • Method and system for optimizing maintenance of mechanical system based on proportional hazard model
  • Method and system for optimizing maintenance of mechanical system based on proportional hazard model

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] A maintenance method for optimizing a mechanical system based on a proportional failure rate model, comprising:

[0047] S1. Establish a proportional failure rate model: establish a Weibull PHM function, a reliability function and a maximum likelihood function, and estimate the parameters in the Weibull PHM function through the maximum likelihood function;

[0048] S2. Collect data: collect historical life data and real-time monitoring data;

[0049] S3. Analyze and process the collected data: process the real-time monitoring data to obtain covariates, perform statistical processing on historical life data, and obtain correct historical life data;

[0050] S4. Bring the covariates and the correct historical life data and the parameters of the Weibull PHM function estimated by the maximum likelihood function into the established reliability function to obtain the reliability of the test time and the preset reliability Compared with the threshold value, the maintenance t...

Embodiment 2

[0065] We will give an overview of the method described above with the optimization of the table change time of a mill-turn machining center. The accumulated data includes fault diagnosis data and monitoring data, and the monitoring data consists of vibration amplitudes in the horizontal, vertical and vertical directions of the workbench. Feature extraction is performed on the raw vibration data in each direction, including 12 time-domain features with mean x m , peak x p , the square root amplitude x ra , the square root mean x rms , variance x v , standard deviation x std , skewness x ske , kurtosis (x k ), the peak index x c , margin index x ma , waveform index x sha and pulse index x i . The first four parameter indicators reflect the vibration amplitude and energy in the time domain, and the remaining indicators represent the distribution of the signal time series in the time domain. In the frequency domain, the wavelet packet method is used to decompose the v...

Embodiment 3

[0076] Embodiment 3 is a system embodiment of Embodiment 1, a maintenance system based on a proportional failure rate model to optimize a mechanical system, such as figure 2 shown, including:

[0077] A maintenance system for optimizing a mechanical system based on a proportional failure rate model, comprising:

[0078] Model building module 1: used to establish a proportional failure rate model: establish a Weibull PHM function, a reliability function and a maximum likelihood function, and estimate the parameters in the Weibull PHM function through the maximum likelihood function;

[0079] Data collection module 2: used to collect data: collect historical life data and real-time monitoring data;

[0080] Analysis and processing module 3: used to analyze and process the collected data: process real-time monitoring data to obtain covariates, perform statistical processing on historical life data, and obtain correct historical life data;

[0081] Decision-making module 4: It ...

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Abstract

The invention discloses a method and system for optimizing maintenance of a mechanical system based on a proportional hazard model. The maintenance method comprises the steps that a proportional hazard model is established; data is collected; analysis processing is conducted on the collected data; statistical processing is conducted on historical lifetime data to obtain correct historical lifetime data; concomitant variables, the correct historical lifetime data and parameters of a Weibull PHM function estimated through a maximum likelihood function are respectively introduced into an established reliability function to obtain the reliability of a testing moment, the reliability is compared with a preset reliability threshold value to calculate maintenance time, and whether the mechanical system needs maintenance or replacement or not is judged according to the maintenance time. By adopting the method, the time for maintenance or replacement of equipment can be accurately calculated, the time for maintenance or replacement of the equipment is accurately calculated, unnecessary economic losses can be avoided, the maintenance time can be shortened, the service life of the equipment can be also prolonged, and the reliability of the equipment can be improved.

Description

technical field [0001] The invention relates to a maintenance method for optimizing a mechanical system, in particular to a maintenance method and system for optimizing a mechanical system based on a proportional failure rate model. Background technique [0002] At present, the service life of almost all equipment depends on the initial design and later use, and the different service conditions in the later period lead to different service life of the equipment, which means that once the equipment is put into use, its service life depends entirely on its work environment and maintenance. It can be seen that correct maintenance is an important factor for equipment life. The traditional condition-based maintenance (CBM) is a very popular maintenance method. When recommending this method, maintenance decisions are made based on the data obtained from condition monitoring. That is to say, preventive maintenance is the key. The so-called preventive maintenance is to combine the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/50G06Q10/00
CPCG06F30/17G06F30/20G06Q10/20
Inventor 李强易永余吴芳基
Owner HANGZHOU ANMAISHENG INTELLIGENT TECH CO LTD
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