Complexity measure: A nonlinear time series analysis technique for machine health monitoring

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Abstract

This paper presents a non-linear time series analysis technique for machine health monitoring, based on the complexity measure. As a statistical parameter, the complexity measure quantifies the randomness or regularity of a time series. After introducing the theoretical framework, numerical simulation of an analytic signal, which is mathematically formulated through a pair of Fourier transform and inverse Fourier transform operations on a measured vibration signal from a rolling bearing, is presented to quantitatively establish the relationship between the severity of signal degradation and the complexity values. The simulation results are then evaluated through experimental study of vibration signals measured during a bearing's run-to-failure test. It has shown that the complexity measure provides an effective technique for monitoring machine health status.

Original languageEnglish
Title of host publicationStructural Health Monitoring 2007
Subtitle of host publicationQuantification, Validation, and Implementation - Proceedings of the 6th International Workshop on Structural Health Monitoring, IWSHM 2007
EditorsFu-Kuo Chang
PublisherDEStech Publications
Pages1291-1298
Number of pages8
ISBN (Electronic)9781932078718
StatePublished - 2007
Externally publishedYes
Event6th International Workshop on Structural Health Monitoring: Quantification, Validation, and Implementation, IWSHM 2007 - Stanford, United States
Duration: 11 Sep 200713 Sep 2007

Publication series

NameStructural Health Monitoring 2007: Quantification, Validation, and Implementation - Proceedings of the 6th International Workshop on Structural Health Monitoring, IWSHM 2007
Volume2

Conference

Conference6th International Workshop on Structural Health Monitoring: Quantification, Validation, and Implementation, IWSHM 2007
Country/TerritoryUnited States
CityStanford
Period11/09/0713/09/07

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