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This paper presents a novel method for the Non-Parametric Modelling of Magneto-Rheological (MR) Dampers using Adaptive Neuro-Fuzzy (NF) that incorporates a Particle Swarm Optimisation (PSO) method. In this approach, the adaptive NF method is using an adaptive Back-Propagation (BP) learning algorithm, which is used to update the weights in real-time. Initial values of the weights and biases are optimised using PSO in an off-line manner. The experimental data was presented in the time histories of the displacement, the velocity and the force parameters, measured for both constant and variable current settings and at a selected frequency applied to the damper. The model parameters were determined using a set of experimental measurements corresponding to different current constant values. It has been shown that the MR damper model's response via the proposed NF approach is in a good agreement with the MR damper test rig counterpart.

Abstract. This paper presents a study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data was acquired from three different types of bearing defect i.e. corroded, outer race defect and point defect. The experiments were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transformed into frequency domain using a frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level was calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show significant deviation in retaining RMS value after a few levels of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended in on-line monitoring while the machine is on operation.

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