Method for predicting remaining life of cold standby system under different failure information conditions
A failure information and life prediction technology, applied in special data processing applications, instruments, CAD technology, etc., can solve problems such as rough accuracy and operability, extensive discussion of system component life distribution, and high requirements for known conditions of cold standby systems
Active Publication Date: 2019-09-20
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 in conjunction with the accompanying drawings and specific embodiments.
[0066] In the embodiment, it is set that the power supply and distribution subsystem of a certain satellite platform contains battery components, which are 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 next battery takes over and continues to work. As long as there are 3 batteries for power supply, the power supply and distribution subsystem can operate normally. This can be used as a 3 out of 7 cold standby system, that is, n=7, k=3. Before the replacement operation occurred, the battery pack system had 3 batteries in normal operation. .
[0067] The working time of the battery module system is set to be 1000 hours, that is, τ=1000; the sampling times of the simulation is 5000, that is, T=5000, and the sampling results at this time are...
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The invention belongs to the field of service life prediction, and particularly relates to a method for predicting the remaining service life of a cold standby system under different failure information conditions, which predicts the remaining service life of the k cold standby system in n according to the integrity of the failure information of the cold standby system. According to the method, a random simulation method is combined, on the basis of service life prediction of the cold standby system, remaining service life distribution is obtained through a selection method, three different failure information integrity conditions are discussed, and finally prediction methods of the remaining service life of the cold standby system under the corresponding conditions are provided. The residual life prediction method is novel in idea and high in operability, can be widely applied to residual life prediction of various cold standby systems, and overcomes the defect that an existing residual life prediction method of the cold standby system is limited to element life obeying index distribution or one cold standby system is selected from n.
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 method is to derive the formula for the remaining life of the product when the life distribution of the product is known, as follows: [0003] [0004] [0005] [0006] Taking the 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 s...
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Login to View More IPC IPC(8): G06F17/50
CPCG06F2111/10G06F30/20
Inventor 宋兆理赵骞姚杭贾祥程志君郭波
Owner NAT UNIV OF DEFENSE TECH