Equivalent health assessment of rotating machinery with imbalance rotor based on metric learning

Abstract

Imbalanced faults are common and highly harmful faults in the rotating machinery, and the causes of imbalance are various. To better establish a unified cognitive form of imbalance fault health states, a series of experiments are designed to explore the signal changes of the rotating system under different imbalance states, and a Mahalanobis distance (MD) metric learning method based on feature extraction in the time domain and frequency domain is proposed. Finally, the mapping relations between unbalance moments and confidence values (CV) are constructed, which the proposed equivalent health assessment (EHA). The verification results prove that the proposed EHA is effective for accurately knowing the health degree of the given rotating system under imbalance states.