Abstract
This paper reports a simple, effective and robust fusion approach based on fuzzy logic and Sugeno-style inference engine. Using this method, four condition-monitoring indicators, developed for detection of transient and gradual abnormalities, are fused into one single comprehensive fuzzy fused index (FFI) for reliable machinery health assessment. This approach has been successfully tested and validated in two different applications: tool condition monitoring in milling operations and bearing condition assessment. The FFI differentiates clearly between the normal and abnormal conditions using the same fuzzy rule base. This certainly shows the versatility and robustness of the FFI. As the FFI value always falls between zero and one, it facilitates threshold setting in monitoring conditions of different tools or machinery components. Our experimental study also indicates that the FFI is sensitive to fault severity, capable of differentiating damages caused by an identical fault at different bearing components, but not susceptible to load changes.
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