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 language | English |
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Title of host publication | Australian Acoustical Society Annual Conference, AAS 2018 |
Place of Publication | Adelaide; Australia |
Publisher | Australian Acoustical Society |
Pages | 313-320 |
Number of pages | 8 |
ISBN (Electronic) | 9781510877382 |
Publication status | Published - 1 Jan 2019 |
Event | Acoustics 2018: Hear to Listen - Adelaide, Australia Duration: 6 Nov 2018 → 9 Nov 2018 https://acoustics.asn.au/conference_proceedings/AAS2018/ |
Publication series
Name | Australian Acoustical Society Annual Conference, AAS 2018 |
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Conference
Conference | Acoustics 2018 |
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Country/Territory | Australia |
City | Adelaide |
Period | 6/11/18 → 9/11/18 |
Other | 2018 Australian Acoustical Society Annual Conference |
Internet address |