Survival analysis method for predicting machine damage time

Abstract

The invention relates to a survival analysis method for predicting machine damage time, which decomposes a survival analysis problem for predicting the machine damage time into sub-problems of a timeslice, and greatly reduces the difficulty of using a neural network to model a long-time sequence prediction problem after decomposing a time sequence prediction problem on the whole time length; therisk probability of each time slice is modeled by using the same neural network, and the final survival probability is obtained through a conditional probability rule. On the premise of not carrying out any assumption on the time distribution of the damage time of the machine, a prediction model can be trained by combining big data. The invention not only can be used for predicting the survival probability of discrete time slices, but also can play a role in predicting the survival probability of continuous time. Experiments prove that the prediction accuracy of the survival analysis model trained through the deep neural network is far higher than that of a traditional method. And through parallel calculation, the algorithm can perform long-distance survival probability prediction under the condition of not increasing the operation time.

Description

technical field

[0001] The invention relates to the modeling of the damage time of machinery and equipment in the engineering field, especially the modeling and research of the problem by using the survival analysis method. Background technique

[0002] In engineering, survival analysis is often used to predict when a machine will fail. Survival analysis is a discipline that studies survival phenomena and response event data and their statistical laws. The subject is a branch of statistics that is widely used in fields such as medicine, biology, and finance.

[0003] Traditional survival analysis methods often require a very strong assumption about the data distribution. For example, the commonly used parameter regression, when using this method, we must first select a distribution, and then use the data to fit the parameters in the distribution equation. There is also a semi-parametric method of the Cox method. This method assumes that the data is distributed with equal ...

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