This work is aimed at developing a new signal processing technology based on time-frequency analysis to extract the dynamic characteristics of rotating machinery and bridge structure. Therefore, the work can be divided into two parts, the condition monitoring of the gearbox and the structural health monitoring of the bridges. The first part of the work aims: (i) to propose a Joint Time-Frequency Analysis (JTFA) method for gear fault diagnosis by using the combined autoregressive (AR) model-based filtering and Reassigned Smoothed Pseudo Wigner-Ville Distribution (RSPWVD) methods; (ii) to investigate the use of both vibration and acoustic measurements for fault diagnosis of a gear system by using the proposed fault diagnosis method. To the best of the author’s knowledge, such RSPWVD method has not been utilized for gearbox applications due to problems with the complexity of signals generated by the gearbox. For this purpose, experiments on a single-stage spur gearbox were carried out on a gearbox test-rig using a single-defect with two different severity levels and double-defect gear tooth faults, utilizing vibration and non-contact acoustic sensing. It was experimentally demonstrated that the proposed fault diagnosis method performed better compared to the Continuous Wavelet Transform, the Smoothed Pseudo Wigner-Ville Distribution and even with the newly introduced parameterized time-frequency method, the General Linear Chirplet Transform. The proposed method can provide a more accurate indication of faults in a gearbox, even for the case of multiple gear defects using both acoustic and vibration measurements. The results demonstrate the potential of using non-contact acoustic measurement using the proposed signal processing method as an alternative sensing method for gear condition monitoring applications.
Conversely, another focus of this research is on the structural health monitoring of a bridge with GNSS (Global Navigation Satellite System) measurements. However, the implementation of the time-frequency analysis methods utilized in the condition monitoring of a gearbox is not possible to apply directly due to the large scale of the data set from GNSS measurements. The restriction from the duration-bandwidth principle does not permit accurate tracking of the variation of the natural frequencies in every single epoch measurement from the lengthy data in GNSS measurements. Therefore, a simple yet efficient algorithm using the Fast Fourier Transform (FFT) method is proposed to capture the shifting phenomena of the natural frequencies of the bridge during three-day measurements. It is shown that a GNSS sensor can provide useful information regarding the shifting of natural frequencies that are affected by the variation of the ambient temperature during the field test.
|Date of Award||15 Jul 2018|
- Univerisity of Nottingham
|Supervisor||Dunant Halim (Supervisor) & Gethin Wyn Roberts (Supervisor)|
- time-frequency analysis
- rotating machinery
Development of time-frequency analysis for extraction of dynamic characteristics for rotating machinery and bridge structures
HARTONO, D. (Author). 15 Jul 2018
Student thesis: PhD Thesis