Cepstral analysis, rooted in speech processing and telecommunications, emerged in the mid-20th century to address challenges in analyzing complex signals, particularly human speech. Initially applied for speech recognition, it evolved to…
Cepstrum analysis is a signal processing technique used in condition monitoring to identify hidden patterns or periodicities in signals, particularly useful for analyzing non-stationary and noisy signals from machinery.
Cepstrum provides insights into the periodicities or repeating patterns within a signal by separating the spectral components from the phase information. It helps identify dominant frequencies, harmonics, and periodic anomalies in signals.
Cepstrum analysis finds applications in various fields such as:
Cepstrum analysis helps identify subtle changes in vibration patterns, revealing hidden faults or abnormalities in rotating machinery, enabling early intervention and preventive maintenance to avoid costly breakdowns and production downtime.
Challenges may include processing complexity, computational resource requirements, selection of appropriate analysis parameters, and interpretation of cepstral features. Overcoming these challenges is crucial for deploying cepstrum-based monitoring systems effectively in industrial environments.
