Integration of Nonlinear Dynamics and Machine learning for Diagnostics of a Single-Stage Gear Box

Abstract


 
 
 The current study concerns diagnostics of a one-stage gear- box based on the integration of physics and machine learn- ing. A physics-based model of this system is developed, then a nonlinear dynamic analysis is performed. The accuracy of the model is validated by comparing fundamental phenomena observed in synthetic and experimental data. To address the diagnostics problem synthetic data are generated for faulty and healthy conditions. Further, physics-informed features are extracted from the phase space of the dynamic system. It is shown that these features are highly informative about the health condition of the system. Also, their advantages over purely statistical features are demonstrated by a feature ranking technique. Subsequently, they are used as inputs in a machine learning model that is developed and optimized for fault diagnostics. The performance of the proposed method is investigated from different aspects, e.g., the accuracy of fault classification, robustness to noise, and generalization to unseen scenarios.