Motor Fault Diagnosis with Wavelet and Hilbert Transform Analysis

When analyzing motor fault signals, wavelet transform can be employed to extract additional information. Beyond fundamental knowledge such as motor diagnostics, detection methods, permissible limits, diagnostic equipment, and diagnostic principles, we must also consider fault types and their impacts. For instance, motor failures may lead to machine downtime, potentially affecting production schedules. Therefore, preventive measures and solutions for motor faults require thorough consideration. In such scenarios, Hilbert transform can be implemented for signal analysis to better identify fault types and consequences, enabling appropriate corrective actions. Typically, Hilbert transform implementation involves envelope detection through the analytic signal calculation: x_analytic = hilbert(x_signal) followed by envelope = abs(x_analytic). Overall, motor fault diagnosis and condition monitoring represent a complex field demanding deep understanding of various techniques and tools to effectively protect equipment and ensure production line stability. Code implementation often involves signal preprocessing, feature extraction using wavelet decomposition (e.g., wavedec function in MATLAB), and fault pattern recognition through time-frequency analysis.