Bahman Ahmadi

Rotary machines represent a versatile class of mechanical equipment, used in a wide range of industrial applications, made vital by their high strength and low cost. Bearings are essential components of rotating machines, which have important functions in various applications, since these machines are used for daily operations. The harsh working environment and long-term operation makes them vulnerable to damage, and due to the importance of bearings, it is very important to identify and fix their faults. So far, various methods have been used to detect defects. whose core is the vibration analysis method, the use of a powerful method can affect the quality of the extracted information. Accurately extracting information from small defects is the key to identifying and diagnosing bearing defects. In this thesis, the "experimental wavelet transform" technique, which is a new method for detecting bearing defects at different rotational speeds, is used, and the signal is decomposed into several combinations and experimental modes are obtained using MATLAB software. Also, for the most suitable component, we apply the elongation parameter on the modes. In order to detect the fault, by applying the Hilbert transform on the determined mode, the Hilbert spectrum is obtained, which reveals the frequencies of the fault. Also, in order to evaluate the proposed method, we apply the used technique first on the simulated fault signal and then on the real signals in order to find the fault of the bearing components. In the continuation of the discussion, the experimental mode analysis method, which is one of the most powerful methods of vibration signal analysis, has been used in order to compare with the proposed method in identifying defects and analyzing vibration signals. The obtained results show that the external, internal rim, rolling element and bearing are healthy. The proposed method is superior to the experimental mode analysis in the detection of bearing defects.