Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

The installation of a condition monitoring system (CMS) aims to reduce the operating costs of the monitored system by applying a predictive maintenance strategy. However, a system-driven configuration of the CMS requires the knowledge of the system kinematics and could induce a lot of false alarms because of predefined thresholds. The purpose of this paper is to propose a complete data-driven method to automatically generate system health indicators without any a priori on the monitored system or the acquired signals. This method is composed of two steps. First, every acquired signal is analyzed: the spectral peaks are detected and then grouped in a more complex structure as harmonic series or modulation sidebands. Then, a time-frequency tracking operation is applied on all available signals: the spectral peaks and the spectral structures are tracked over time and grouped in trajectories, which will be used to generate the system health indicators. The proposed method is tested on real-world signals coming from a wind turbine test rig. The detection of a harmonic series and a modulation sideband reports the birth of a fault on the main bearing inner ring. The evolution of the fault severity is characterized by three automatically generated health indicators and is confirmed by experts.