Extracting vibration signal from measured data polluted by thermal noise using a Kalman filtering technique

Bin Wu, Kaichen Song, Jie Pan

Research output: Chapter in Book/Conference paperConference paper

Abstract

In practice, a measured vibration signal is often mixed with the inherent thermal noise existing in the measurement system. It is difficult to use traditional frequency-domain filtering techniques as the desired vibration signal and the unwanted thermal noise may have components in the same frequency band. In this research, a dual-sensor vibration measurement system and a Kalman filter based on a linear prediction model are developed to reduce the thermal noise in measured data. This paper presents a mathematical analysis of the linear-prediction-based Kalman filter and examines the effects of the prediction error and measurement error on the filtering performance. The results show that the linear-prediction-based Kalman filter can reduce the prediction error compared to the traditional random-walk model. The effect of unsteady measurement error on filtering performance is also investigated. A simulation example is used for illustration. The simulation result shows that the linear-prediction-based Kalman filter achieves a better anti-drift performance than the conventional low-pass filter, and the delay of the linear-prediction-based Kalman filter is smaller than that of the conventional low-pass filter.

Original languageEnglish
Title of host publicationAustralian Acoustical Society Annual Conference, AAS 2018
Place of PublicationAdelaide; Australia
PublisherAustralian Acoustical Society
Pages313-320
Number of pages8
ISBN (Electronic)9781510877382
Publication statusPublished - 1 Jan 2019
Event2018 Australian Acoustical Society Annual Conference, AAS 2018 - Adelaide, Australia
Duration: 6 Nov 20189 Nov 2018

Publication series

NameAustralian Acoustical Society Annual Conference, AAS 2018

Conference

Conference2018 Australian Acoustical Society Annual Conference, AAS 2018
CountryAustralia
CityAdelaide
Period6/11/189/11/18

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Wu, B., Song, K., & Pan, J. (2019). Extracting vibration signal from measured data polluted by thermal noise using a Kalman filtering technique. In Australian Acoustical Society Annual Conference, AAS 2018 (pp. 313-320). (Australian Acoustical Society Annual Conference, AAS 2018). Adelaide; Australia: Australian Acoustical Society.