An improved OAKR approach to condition monitoring of rotating machinery

Abstract

Faults in main subsystems or components of a rotating machine often causes unscheduled shutdown, which may lead to not only huge economic losses, but also safety accidents. As an important part of intelligent maintenance, condition monitoring becomes a powerful tool in reducing maintenance costs through automatic fault alarming, thereby reducing potential downtime while improving system safety and reliability. An optimized auto-associative kernel
regression (OAKR) model has been proposed recently and demonstrated as a promising tool for condition monitoring of various turbomachines, which is independent of fault mode and machine type. However, the fault identification accuracy of this approach largely relies on data quality in practical applications. Data incompleteness, parameter variation and system complexity often result in the inaccuracy of fault alarming for complicated rotating machinery. This paper proposes an improved OAKR method to address these issues, including utilizing wavelet packet Bayesian thresholding method (WPB) to reduce noise in the raw multivariate data, developing the Manhattan distance to calculate the sample similarity, and constructing a multivariate health index based on Multivariate Permutation Entropy to identify potential faults in equipment condition monitoring. Parametric analysis and a comparison study with original AAKR and OAKR methods by using the actual data of a gas turbine are conducted to illustrate the effectiveness and feasibility of the proposed methodology.