Rolling Bearing Outer Race Fault Frequency Analysis using Wavelet and Envelope Transform

This content discusses the fault frequency characteristics of rolling bearing outer races, processed through wavelet analysis followed by envelope transformation. The experimental data originates from Case Western Reserve University's bearing dataset. Further technical exploration could include: - Implementation methods for other bearing fault types and their characteristic frequencies using Fast Fourier Transform (FFT) and order analysis algorithms - Detailed explanation of wavelet decomposition principles (e.g., using MATLAB's wavedec function) and envelope analysis techniques (Hilbert transform implementation) for fault feature extraction - Investigation of CWRU's research methodology, experimental setup including drive-end bearing test rig specifications, and data acquisition parameters - Additional contributions in rolling bearing diagnostics such as deep learning approaches for fault classification and prognosis algorithms Code implementation typically involves signal preprocessing, wavelet coefficient calculation using db4 or sym4 wavelets, and envelope detection through Hilbert transform with frequency spectrum analysis for characteristic frequency identification.