Residual life prediction method for non-stationary degradation process with uncertain impact

A life prediction and non-stationary technology, applied in design optimization/simulation, special data processing applications, instruments, etc., can solve problems such as equipment impact and achieve accurate online remaining life prediction results

Active Publication Date: 2017-09-08
ZHEJIANG UNIV +1
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Problems solved by technology

[0003] In view of the existing technical situation, the purpose of the present invention is to solve the problem of impact phenomenon in the operation process of the equipment, according to the available performanc

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  • Residual life prediction method for non-stationary degradation process with uncertain impact
  • Residual life prediction method for non-stationary degradation process with uncertain impact
  • Residual life prediction method for non-stationary degradation process with uncertain impact

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[0065] The specific implementation of the present invention will now be further described in conjunction with the accompanying drawings. Part of the principles have been described in detail above and will not be repeated here. The following example uses a real case based on milling machine data to illustrate the specific operation steps and verify the performance of the proposed model.

[0066] This milling machine data records the degradation of the operation of cutting metallic materials with milling cutters. The case includes 16 different operating conditions, variables including depth of cut, material properties, feed rate, etc. Each case contains multiple cutting processes and terminates when the equipment reaches the failure threshold. This example takes a case as an example and uses the signal of its acoustic wave sensor as the degradation indicator. In order to better verify the performance of the model, this example uses another effective adaptive model (denoted as S...

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Abstract

The invention provides a residual life prediction method for a non-stationary degradation process with uncertain impact and belongs to the technical field of reliability engineering. The impact effect is combined with a continuous degradation process, thereby more reasonably describing the actual degradation process. In a state estimation phase, a new online state estimation algorithm is provided to identify the real system state and to provide a necessary support for subsequent residual life prediction. In a parameter estimation stage, unknown parameters in a model are obtained by adopting an expectation maximization algorithm. For residual life production, the uncertainty of state estimation and the influence of the impact effect are considered at the same time, and an expression is analyzed to give a probability density function and a cumulative probability density function of residual life prediction distribution. The provided model meets the actual degradation condition, a more accurate residual life prediction result can be obtained, and the residual life prediction method is of great significance in fault prediction and health management in engineering.

Description

technical field [0001] The invention belongs to the technical field of reliability engineering and relates to a method for predicting the remaining life of a non-stationary degradation process with uncertain impact. Background technique [0002] Equipment degradation and failure phenomena are unavoidable for most industrial equipment in operation. Real-time status monitoring of online equipment and timely assessment of equipment health status, and then predicting its remaining life can significantly reduce operation and maintenance costs and ensure equipment operation safety. At present, prediction methods based on physical modeling have been difficult to adapt to the complex situation of industrial equipment. The data-driven modeling method based on stochastic processes takes advantage of the advantages of sensing technology, and is becoming more and more popular among researchers. For different operational degradation characteristics, a large number of different predicti...

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 徐正国王豆柯晓杰陈积明秦刚华谢尉扬胡伯勇张震伟孙优贤
Owner ZHEJIANG UNIV
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