Remaining Life Prediction Method of Cold Standby System under Different Failure Information Conditions
A failure information and life prediction technology, applied in the direction of instruments, design optimization/simulation, CAD technology, etc., can solve problems such as rough accuracy and operability, complex cold standby system, and extensive discussion on the life distribution of system components
Active Publication Date: 2020-03-27
NAT UNIV OF DEFENSE TECH
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Problems solved by technology
For the general k-out-of-n cold standby system, although this simulation method based on complex systems can also be applied, it fails to consider different failure information knowledge conditions at the current prediction time, in terms of accuracy and operability relatively rough
[0022] At present, because the structure function of the cold standby system is more complex than the typical structure, there are few researches on the life and remaining life of the cold standby system at home and abroad.
Some of the only studies, either have too high requirements on the known conditions when predicting the remaining life of the cold standby system, or fail to discuss the life distribution of system components extensively, but only stay in the case that the life distribution of components obeys the exponential distribution
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[0065] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0066] In the embodiment, it is set that the power supply and distribution system of a satellite platform includes a battery component, which is composed of 7 battery components produced in the same batch and of the same model. Once a battery fails, it will be in a cold standby state The lower battery takes over and continues to work. As long as there are 3 batteries to supply power, the power supply and distribution system can run normally. This can be used as a cold standby system of 3 out of 7, ie n=7, k=3. The battery pack system had 3 batteries in normal operation before the replacement operation took place. .
[0067] It is assumed that the working time of the battery assembly system is 1000 hours, that is, τ=1000; the sampling times of the simulation are 5000 times, that is, T=5000, and the sampling results at this time are bas...
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The invention belongs to the field of life prediction, and specifically relates to a method for predicting the remaining life of a cold standby system under different failure information conditions, which predicts the remaining life of a cold standby system with k out of n according to the completeness of the failure information of the cold standby system. The present invention combines the stochastic simulation method, on the basis of the life prediction of the cold standby system, adopts the selection method to obtain the distribution of the remaining life, and discusses three different failure information integrity conditions, and finally gives the cold A method for predicting the remaining life of a backup system. The invention has a novel idea and strong operability, and can be widely used in the remaining life prediction of various cold standby systems, and overcomes the limitation that the remaining life prediction method of the existing cold standby system is limited to the component life obeying the exponential distribution, or taking 1 out of n cold system flaws.
Description
technical field [0001] The invention belongs to the field of life prediction, and in particular relates to a method for predicting the remaining life of a cold standby system under different failure information conditions. Background technique [0002] Remaining life is an important indicator for evaluating product reliability. Remaining life prediction refers to the quantitative evaluation of the remaining life of service components. The traditional approach is to derive the formula for the remaining life of the product given the known product life distribution, as follows: [0003] [0004] [0005] [0006] Taking Weibull distribution as an example, its cumulative probability density function is [0007] [0008] where t is the failure time, λ is the scale parameter, and β is the shape parameter. [0009] Its probability density function is [0010] [0011] On the basis of the existing engineering system, some redundant components with the same or even...
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IPC IPC(8): G06F30/20G06F111/10
CPCG06F2111/10G06F30/20
Inventor 宋兆理赵骞姚杭贾祥程志君郭波
Owner NAT UNIV OF DEFENSE TECH