Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—The rotating machine contains the many rotating parts and one rotating part produces additional noise to the others. As a result, fault signatures of the rotating machine are turned out to be quite weak. This paper proposed an effective method to detect the fault signatures of rotating machines based on improved adaptive filter, fuzzy logic and spectrum analysis. An improved adaptive filter is used to remove the noises from the faulty signal. Since the performance of the adaptive filter depends on the step size, a new technique is proposed to select the step size effectively based on entropy and fuzzy logic. To determine the fault signature of rotating machines of vibration signals effectively, demodulation is often required. Both squared envelope and Hilbert based envelope analysis are performed to identify the fault signature accurately. The effectiveness of the proposed adaptive filter is shown by simulation. Performances of the improved adaptive are also verified by real experimental data. Experimental results show that the proposed method can effectively detect the fault signatures of the rotating machines.
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