A Survival Analysis Method for Predicting Machine Damage Time

Abstract

A survival analysis method for predicting machine damage time. The survival analysis problem of predicting machine damage time is decomposed into sub-problems of component time slices, and the time series prediction problem over the entire time length is decomposed to greatly reduce the problem. The use of neural network modeling The difficulty of long-term series prediction problem, by using the same neural network to model the risk probability of each time slice, the final survival probability is obtained through the conditional probability rule. Without making any assumptions about the temporal distribution of the damage time of the machine, a prediction model can be trained in combination with big data. Not only can it be used in the prediction of survival probability in discrete time slices, but also in prediction of survival probability in continuous time. Experiments show that the prediction accuracy of survival analysis model trained by deep neural network is far better than traditional methods. And through parallel computing, the algorithm can perform long-distance survival probability prediction without increasing the computing time.

Description

technical field [0001] The invention relates to the modeling of the damage time of machinery and equipment in the field of engineering, in particular to modeling and researching the problem by using a survival analysis method. Background technique [0002] In engineering, survival analysis is often used to predict when machines 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 medicine, biology, finance and other fields. [0003] Traditional survival analysis methods often require a very strong assumption about the distribution of the data. For example, the commonly used parameter regression, when using this method, we must first select a distribution, and then fit the parameters in the distribution equation through the data. There is also a semi-parametric method of the Cox method, which assumes that the data is distributed in equal ...

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Problems solved by technology

[0005] 1. The Chinese invention patent "A Time Series Deep Survival Analysis System Combined with Active Learning" with the application number CN111312393A proposes a survival analysis system combined with active learning. This method is mainly aimed at discrete time domains and is not suitable for continuous time domains.

[0006] 2. The Chinese invention patent "Construction of Survival Analysis Model, Survival Rate Prediction Method, Device and Equipment" with application number CN111243738A proposes a survival analysis model, but this method cannot be modeled for the unique characteristics of a single patient. Can only do coarse-grained modeling

[0008] 1. Kan Ren et al. published Deep Recurrent Survival Analysis "Deep Recurrent Survival Analysis Neural Network" at the 33rd session of the Association for the Advancement of Artificial Intelligence (Association for the Advancement of Artificial Intelligence). This paper uses a recurrent neural network for the survival analysis problem to deal with, its shortcoming is that the output delay of the result is too long

[0009] 2. DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks published by Changhee Lee et al. at the 32nd session of the Association for the Advancement of Artificial Intelligence (Association for the Advancement of Artificial Intelligence), the article Modeling the survival analysis problem with a neural network has the disadvantage of not being able to fully use the information in the data

[0010] The following conclusions can be drawn from the analysis of relevant domestic and foreign patents and related research: At present, in the field of survival analysis and prediction, there is no fine-grained survival analysis and prediction model that is applicable to both continuous time domain and discrete time domain

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