机械故障检测使用模糊的索引融合外文文献翻译、中英文翻译.docVIP

Mechanical fault detection using fuzzy index fusion Tony BoutrosHYPERLINK \l /implicit0a and Ming Liang aDepartment of Mechanical Engineering, University of Ottawa, 770 King Edward Avenue, Ottawa, Ont., Canada K1N 6N5 Received 22 June 2006;? revised 26 December 2006;? accepted 3 January 2007.? Available online 23 January 2007. 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. Keywords: Condition indicators; Fuzzy fusion; Sugeno inference engine; Tool condition; Bearing condition Article Outline Machinery fault detection and machining process monitoring have attracted considerable attention. These tasks have become increasingly difficult due to the complexity of machine structure and operation dynamics. Over the last few decades, many different sensors and condition indicators have been developed in an attempt to achieve more reliable results for different monitoring tasks. For machinery fault detection, Collacott HYPERLINK \l /bib1[1] used the probabilit