A New Probabilistic Kernel Factor Analysis for Multisensory Data Fusion: Application to Tool Condition Monitoring

The features extracted from multisensory measurements can be used to characterize machinery conditions. However, the nonlinearity and uncertainty presented in machinery degradation process pose challenges on feature selection and fusion in machinery condition monitoring. To alleviate these issues, this paper presents a new probabilistic nonlinear feature selection and fusion method, named probabilistic kernel factor analysis (PKFA). First, the mathematical structure of the PKFA is formulated incorporating kernel techniques on the basis of conventional factor analysis (FA). Next, a PKFA-based machining tool condition monitoring model with support vector regression is presented. The effectiveness of the scheme is experimentally verified on a machining tool testbed. The experimental results show that the proposed PKFA method provides more accurate tool condition prediction than using all initially extracted features and other feature selection techniques (e.g., kernel principal component analysis and conventional FA), and thus confirms its utility as an effective tool for machining tool condition assessment.