Hybrid dynamic data-driven approach for drift-like fault detection in wind turbines

Abstract

This paper presents a hybrid dynamic data-driven approach to achieve drift like fault detection of systems evolving in non-stationary environments. This approach considers the system switching between several control modes. This switching is entailed by changes in the system environments. In each control mode, the system has a different dynamical behavior. The latter is described in a feature space sensitive to normal operating conditions in the corresponding control mode. These operating conditions are represented by restricted zones in the feature space called classes. The latter are characterized by a set of parameters representing their statistical properties, e.g. gravity center and variance–covariance matrix. The occurrence of an incipient fault entails a drift in the system operating conditions until the failure takes over completely. This drift manifests as a progressive change in the classes parameters in each control mode over time. The proposed approach monitors normal classes’ parameters in order to detect a drift in their characteristics. This drift detection allows achieving the fault in its early stages. It uses two drift indicators. The first indicator detects the drift and the second indicator confirms it. Both indicators are based on the observation of changes in the normal operating conditions characteristics over time. A wind turbine simulator is used to validate the performance of the proposed approach.