Failure prediction method based on nonlinear failure reconstruction

A technology for fault reconstruction and fault prediction, applied in special data processing applications, instruments, electrical digital data processing, etc.

Inactive Publication Date: 2014-04-09
BEIJING INFORMATION SCI & TECH UNIV
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Problems solved by technology

(2) Nonlinear problems
The result of traditionally ignoring nonlinear factors is often unsatisfactory. With the increasing...

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  • Failure prediction method based on nonlinear failure reconstruction
  • Failure prediction method based on nonlinear failure reconstruction
  • Failure prediction method based on nonlinear failure reconstruction

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Embodiment Construction

[0032] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0033] The present invention provides a kind of fault prediction method based on nonlinear fault reconstruction, which comprises the following steps:

[0034] 1) Use the Kernel Principal Component Analysis (KPCA) model to conduct offline nonlinear modeling of the process data monitored during the operation of the rotating machinery system, and perform anomaly detection to extract fault information;

[0035] assuming x 1 , x 2 ,...,x n ∈ R m It is n m-dimensional column vector samples for nuclear principal component analysis learning, the nonlinear mapping is φ, and the original space R is mapped to the high-dimensional feature space F through φ high to go. raw data x i In the high-dimensional feature space F high The image in φ(x i ), record Φ=[φ(x 1 ),φ(x2 ),...,φ(x n )] T . The KPCA modeling process is as follows:

[0036] C φ v=λv / n, ...

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Abstract

The invention relates to a failure prediction method based on the nonlinear failure reconstruction. The method comprises the following steps that (1), a KPCA model is utilized for carrying out off-line nonlinear modeling on process data monitored when a rotary mechanical system operates, carrying out abnormity detection and extracting failure information, wherein the KPCA model is the kernel principal component analysis model; (2), the failure degree is quantificationally described through the KPCA model and the abnormity detection in the step (1), and failure estimation is achieved under the failure reconstruction through the optimization method; (3), the development tendency of the estimated failure amplitude f is predicted through the multi-layer hierarchical method. The failure prediction method based on the nonlinear failure reconstruction can effectively improve the failure prediction efficiency, reduce false alarm rate and omitted alarm rate and lay the foundation for further researching the prediction maintenance technology of a complex mechanical system. The failure prediction method based on the nonlinear failure reconstruction can also be widely applied to online monitoring systems of large electromechanical devices of petrochemical enterprises, metallurgy enterprises, coal enterprises and other many enterprises.

Description

technical field [0001] The invention relates to a fault prediction method, in particular to a fault prediction method based on nonlinear fault reconstruction in the technical field of mechanical fault detection. Background technique [0002] With the rapid development of science and technology and modern industry, the machinery, energy, petrochemical, transportation and national defense industries of the national economy are increasingly large-scale, high-speed, integrated and automated. Once an accident occurs in such a complex system, it will cause Huge property loss and casualties. Therefore, preventing safety issues caused by component performance degradation has become an important problem in the daily operation of complex systems. As an important part of predictive maintenance, fault prediction has been widely concerned by experts at home and abroad. [0003] In the complex industrial process environment, the traditional fault diagnosis technology based on the proces...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 马洁
Owner BEIJING INFORMATION SCI & TECH UNIV
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