Model development based on evolutionary framework for condition monitoring of a lathe machine

The present work deals with the vibro-acoustic condition monitoring of the metal lathe machine by the development of predictive models for the detection of probable faults. Firstly, the experiments were conducted to obtain vibration and acoustic signatures for the three operations (idle running, turning and facing) used for three experimental studies (overall acoustic, overall vibration and headstock vibration). In the perspective of formulating the predictive models, multi-gene genetic programming (MGGP) approach can be applied. However, it is effective functioning exhibit high dependence on the complexity term incorporated in its fitness function. Therefore, an evolutionary framework of MGGP based on its new complexity measure is proposed in formulation of the predictive models. In this proposed framework, polynomials known for their fixed complexity (order of polynomial) are used for defining the complexity of the generated models during the evolutionary stages of MGGP. The new complexity term is then incorporated in fitness function of MGGP to penalize the fitness of models. The results reveal that the proposed models outperformed the standardized MGGP models. Further, the parametric and sensitivity analysis is conducted to study the relationships between the key process parameters and to reveal dominant input process parameters.