Abstract
This PhD project focuses on developing online monitoring approaches for suspension systems based on vibration analysis, aiming at guaranteeing the safe and efficient operations of vehicles including the railway and autonomous vehicles.Based on Operational Modal Analysis (OMA), which has been proven more effective in the field of structural health monitoring, a novel OMA algorithm, entitled the Correlation Signal Subset-based Stochastic Subspace Identification (CoSS-SSI), is proposed in this thesis to identify the inherent vibration modes of a car body and railway bogie frame to assess the health of the vehicle suspension system. The proposed novel OMA method is developed in the knowledge that the basic framework of SSI makes it applicable to nonlinear systems with nonstationary responses in the presence of high noise levels.
For the CoSS-SSI method, the measured raw signals are divided into short segments; then the correlation function of each data segment is calculated, which performs the first noise reduction. After that, the obtained correlation function for the segments are divided into subsets according to their minimum amplitudes, and then each subset is averaged to further reduce the noise. Different correlation signal subsets can reduce nonlinear effects such as high damping, on OMA. Lastly, each subset of the averaged correlation signals is utilised to accurately identify the modal parameters based on SSI.
A 3-DOF vibration system was developed in an initial simulation study developed to evaluate the performance of CoSS-SSI, which showed that CoSS-SSI was superior to other
conventional OMA methods like Cov-SSI in extracting useful modal information on system behaviour. In addition, a quarter vertical vehicle model was constructed to investigate the effects of periodic pulses and harmonics on OMA. It was found that periodic pulses have no impacts on OMA, but harmonics can cause significant adverse effect. Then, cepstrum editing was introduced to eliminate the harmonic effect on OMA, and its performance was first verified by simulated data and later by experimental data obtained from full-scale rig tests.
Experimental studies were carried out to verify the feasibility of applying the proposed method for the online monitoring of suspension systems. In the first set of experiments,
accelerometers were installed at the four corners of a car body and it was shown that with CoSS-SSI these comprised a robust and cost-efficient system for monitoring the suspension system. These results were confirmed by using CoSS-SSI to identify the modal parameters of a road vehicle suspension using the measured vibrations of a real car running normally on a traditional country road near Huddersfield, UK. These experiments confirmed that CoSSSSI had the capability to extract the inherent vibration modes of the vehicle suspension system.
Importantly, a 1/5th scale roller rig and then an Y25 bogie were employed to verify the potential of CoSS-SSI for railway vehicle suspension monitoring. The outcomes from roller rig experiments showed that the novel CoSS-SSI proposed here is also feasible for the successful online monitoring of railway vehicle suspension systems.
| Date of Award | 5 May 2020 |
|---|---|
| Original language | English |
| Supervisor | Andrew Ball (Main Supervisor) & Fengshou Gu (Co-Supervisor) |