Development of time-frequency analysis for extraction of dynamic characteristics for rotating machinery and bridge structuresTools Hartono, Dennis (2018) Development of time-frequency analysis for extraction of dynamic characteristics for rotating machinery and bridge structures. PhD thesis, University of Nottingham.
AbstractThis 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.
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