Failure prediction method based on ICA reconstruction

Abstract

The invention discloses a failure prediction method based on ICA reconstruction, which includes the following steps: step 1, calculating a separative matrix W; step 2, calculating the statistic value I<2>(k), SPE(k) or I<2>e(k) of the real-time data Xnew(k) through adopting the formulas I<2>(k)=S'newd(k)<T> S'newd(k), I<2>e=S'newe(k)<T>*S'newe(k), SPE(k)=(xnew(k)-x'new(k))<T>*(xnew(k)-x'new(k)), S'newd(k)=Wd*xnew(k), and S'newe(k)=We*xnew(k), wherein Wd refers to the matrix formed by the lines expect the first d lines of the separative matrix W, We refers to the matrix formed by the lines except the first d lines of the separative matrix W, and X'new(k)=Q<-1>BdWd*xnew(k), Bd=(WdQ<-1>)<T>, Be=(WeQ<-1>)<T>, and Q refers to a whitening matrix; and step 3, calculating the nuclear density of I<2>(k), SPE(k) or I<2>e(k), and detecting failures as per the control limit. The method provided by the invention solves the problem that the traditional flue gas turbine prediction method can not utilize the multidimensional valid data, takes the multi-channel vibration data into consideration, can be used for directly predicating failures, and improves the prediction accuracy compared with the PCA reconstruction method.

Description

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AI Technical Summary

Benefits of technology

This technology helps solve problems by taking advantage of multiple dimensions for accurate predictions while also considering different types of noise sources such as bouncing or other movements caused during exercise activities. It uses both linear regression analysis (LSA) methods and nonlinear filtering techniques like wavelets to improve its performance over time without making it more complicated than usual.

Problems solved by technology

This patents discusses the problem of detecting and diagnosing critical systems like rotational machines during their manufacture without causing any harm caused by external causes like machine malfunctions or incorrect maintenance procedures. Traditional approaches involve taking measurements from these sources and analyzing them manually overall, but this approach requires significant human resources and takes longer than necessary before identifying potential issues. There is currently no efficient way to accurately identify key components involved in critical operations while avoiding unnecessary inspections.

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