Faults detection and classification in a centrifugal pump from vibration data using markov parameters

Abstract One of the strategies to detect and classify faults in mechanical systems is to use a time domain family of techniques known as output-only methods. Those methods are based on the analysis of sample covariance matrices, which are estimated from vibration data extracted from mechanical systems under unmeasured natural excitation. Using the stochastic realization theory, it is possible to derive Markov parameters from sample covariance matrices. Those parameters contain only the significant spectral components from data. In this paper, a novel output-only method based on the Markov parameters is proposed to diagnose faults. The idea is to use the Markov parameters estimated from vibration data as features in classification algorithms based on convex optimization. The method was applied to diagnose incipient cavitation failures in a water supply network centrifugal pump. A low-cost triaxial vibration sensor developed by one of the authors was used to register the vibration data. The proposed method was compared to the analysis based on sample covariance matrices demonstrating the advantages related to the use of the Markov parameters.