Diagnosis and Prognosis of Mechanical Components Using Hybrid Methods

Abstract

Diagnosis and prognosis of mechanical components are important for critical rotating machinery found in the power generation, mining, and aviation industries. Data-driven diagnosis and prognosis methods have much potential; however, their performance is dependent on the quality of historical data. Usually only limited historical data are available for newly commissioned parts and for parts that do not go through a full degradation cycle before being replaced. Physics-based diagnosis and prognosis methods require assumptions of the underlying physics; the governing equations need to be derived and solved; and the model needs to be calibrated for the underlying system. Physics-based methods require extensive domain knowledge and could have modelling biases due to missing physics. Hybrid methods for diagnosis and prognosis of mechanical components have the potential for improving the accuracy and precision of remaining useful life (RUL) estimation when historical fault data are scarce. This is because hybrid methods combine data-driven and physics-based models to alleviate the shortcomings of the respective methods. For these reasons, hybrid methods are getting more attention in the condition monitoring community as a solution for diagnosis and prognosis tasks. Therefore, in this chapter, we present a review of the state-of-the-art implementations of physics-based, data-driven, and hybrid methods for diagnosis and prognosis. The methods are organised using a condition monitoring framework and contributions of various techniques are discussed. We identify gaps in the hybrid diagnosis and prognosis field that could be the focus of future research projects.