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Fault detection and diagnosis based on principal component analysis

Fault detection and diagnosis based on principal component analysis

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In this chapter, PCA, relative PCA (RPCA) [37,38] and normalization PCA (NPCA) are introduced with application in fault detection and fault diagnosis. There are some theories and applications about PCA, such as the basic principles of PCA, geometrical interpretation of PCA, Hotelling's T2 statistic and SPE statistic for fault detection's control limit. Then a fault detection method based on PCA is introduced for Tennessee Eastman (TE) process. What's more, the fault diagnosis method based on PCA is introduced with its application for inverter. There are some theories and application about RPCA, such as the definition of Relative Transform, basic principles of RPCA and geometrical interpretation of RPCA. Then the fault detection method based on RPCA is introduced with its application. In addition, in order to improve the control limit of PCA with Hotelling's T2, the dynamic data window control limit algorithm based on RPCA is introduced with its application. As follows, the fault diagnosis method based on RPCA is introduced with its application. There are some theories and application about NPCA, such as the definition of longitudinal standardization (LS) and basic principles of NPCA. Next a fault detection method based on NPCA is presented with its application in wind power generation. Then another fault detection method based on NPCA is presented with its application in DC motor. In order to increase the control limit of PCA with Hotelling's T2, a fault detection method based on NPCA-adaptive confidence limit (ACL) is presented with application in DC motor.

Chapter Contents:

  • 5.1 Introduction
  • 5.2 PCA and its application
  • 5.2.1 PCA method
  • 5.2.2 The geometrical interpretation of PCA
  • 5.2.3 Hotelling's T2 statistic, SPE statistic and Q–Q plots
  • 5.2.4 Fault detection based on PCA for TE process
  • 5.2.4.1 Case study on Fault 4
  • 5.2.4.2 Case study on Fault 11
  • 5.2.5 Fault diagnosis based on PCA for multilevel inverter
  • 5.2.5.1 Time–frequency transform based on FFT
  • 5.2.5.2 FDD based on PCA
  • 5.2.5.3 Experimental tests
  • 5.3 RPCA and its application
  • 5.3.1 RPCA method
  • 5.3.1.1 Relative Transform
  • 5.3.1.2 Computing RPCs
  • 5.3.2 The geometrical interpretation of RPCA
  • 5.3.3 Fault detection based on RPCA for assembly
  • 5.3.4 Dynamic data window control limit based on RPCA
  • 5.3.5 Fault diagnosis based on RPCA for multilevel inverter
  • 5.4 NPCA and its application
  • 5.4.1 NPCA method
  • 5.4.2 Fault detection based on NPCA for wind power generation
  • 5.4.3 Fault detection based on NPCA for DC motor
  • 5.4.4 ACL based on NPCA
  • 5.4.5 Fault detection based on NPCA-ACL for DC motor
  • 5.5 Conclusions and future works
  • References

Inspec keywords: principal component analysis; power generation faults; transforms; invertors; fault diagnosis; wind power plants; DC motors

Other keywords: ACL; fault diagnosis method; RPCA; relative principal component analysis; fault detection method; TE process; LS; Hotelling's T2 statistics; Tennessee Eastman process; dynamic data window control limit algorithm; SPE statistics; NPCA-adaptive confidence limit; relative transform; normalization principal component analysis; DC motor; longitudinal standardization; inverter

Subjects: Other topics in statistics; Integral transforms; d.c. machines; Wind power plants

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