为了研究条形沟槽深度对船舶水润滑尾轴承摩擦磨损性能的影响,在轴承试件表面分别添加0.5 mm、1.0 mm和1.5 mm 3种不同深度的条形沟槽,并加入1组不添加表面纹理的试件作为对比。使用CBZ-1船舶轴系摩擦磨损试验机对材料进行测试,并结合...为了研究条形沟槽深度对船舶水润滑尾轴承摩擦磨损性能的影响,在轴承试件表面分别添加0.5 mm、1.0 mm和1.5 mm 3种不同深度的条形沟槽,并加入1组不添加表面纹理的试件作为对比。使用CBZ-1船舶轴系摩擦磨损试验机对材料进行测试,并结合表面形貌及磨损量比较分析其摩擦磨损性能。结果表明,条形沟槽可有效提高材料的摩擦磨损性能且沟槽深度对材料性能有着较大影响:转速较低时,较深的沟槽深度更能提升材料的摩擦磨损性能;转速较高时,较浅沟槽深度的材料具有更加优良的性能。展开更多
Condition based maintenance（CBM） issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring m...Condition based maintenance（CBM） issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.展开更多
文摘为了研究条形沟槽深度对船舶水润滑尾轴承摩擦磨损性能的影响,在轴承试件表面分别添加0.5 mm、1.0 mm和1.5 mm 3种不同深度的条形沟槽,并加入1组不添加表面纹理的试件作为对比。使用CBZ-1船舶轴系摩擦磨损试验机对材料进行测试,并结合表面形貌及磨损量比较分析其摩擦磨损性能。结果表明,条形沟槽可有效提高材料的摩擦磨损性能且沟槽深度对材料性能有着较大影响:转速较低时,较深的沟槽深度更能提升材料的摩擦磨损性能;转速较高时,较浅沟槽深度的材料具有更加优良的性能。
基金supported by National Natural Science Foundation of China(Grant Nos.50905135,51275381)
文摘Condition based maintenance（CBM） issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.
