Early fault diagnosis of rotating machinery based on composite zoom permutation entropy

Fault diagnosis of rotating machinery serves an important role in informing system operation and predictive maintenance decisions. To quantify the fault information from vibrational signals, the multiscale permutation entropy has become a promising tool for fault diagnosis of rotating machinery. However, multiscale permutation entropy fails to extract weak features of early faults because it can hardly capture the tiny oscillation patterns of signals over the full frequency band. To address this issue, this paper presents an effective feature extraction method called composite zoom permutation entropy. First, composite zoom permutation entropy employs multiple wavelets to capture complete fault features with multiple resolutions over the full frequency band. Then the composite analysis is performed to improve the separability of extracted features for identifying different early faults. Based on composite zoom permutation entropy, a diagnosis framework has been developed to improve the operational reliability of rotating machinery by identifying faults as early as possible. The simulation results show that composite zoom permutation entropy has better extraction ability compared with other permutation entropy based methods. The experimental results show that the proposed method outperforms existing methods in identifying early faults of rotating machinery.