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  • 1
    ISSN: 0036-8792
    Source: Emerald Fulltext Archive Database 1994-2005
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Purpose - The present paper proposes a theoretical analysis of the stability characteristics of a Jeffcott rotor-hybrid bearing system. It is intended that on the basis of the numerical results drawn from this study, appropriate recess depth, land size, orifice location and speed parameter for stable operation can be determined for use in the bearing design process. Design/methodology/approach - A Jeffcott rotor supported by hybrid oil film bearings with dual-row recesses and orifice- compensated restrictors is studied. In order to facilitate the calculation of film dynamics, using the perturbation method, the Reynolds equation was linearized and subsequently solved using finite difference techniques, whilst the stability maps were determined by the Routh-Hurwitz method. Findings - The data reported here suggest that the stability characteristics of the Jeffcott rotor-bearing system could be improved by the use of shallow, dual-recessed hybrid bearings with orifice compensation. In addition to greater eccentricity ratios, smaller land-width ratios and greater shaft stiffness may also provide shallow-recessed bearings with better stability. In all cases, the stability provided by upstream orifice-sited bearings is better than that provided by center orifice-sited bearings, whilst high-speed parameters may also provide a greater stability threshold. Originality/value - This study proposes an extensive database as a critical requirement in the design of hybrid bearings, in order to ensure that a rotor bearing system is operating stably.
    Type of Medium: Electronic Resource
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  • 2
    ISSN: 0036-8792
    Source: Emerald Fulltext Archive Database 1994-2005
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: All deep-groove ball bearings have similar features in geometry, mechanism, and structure. Stiffness of this type of bearings is related to geometry, dimensions, and operating conditions by a very complex, high-order and coupled-variable function. This paper has verified that the stiffness function for all deep-groove ball bearings can be replaced by a back-propagation neural network (BPNN) which is trained by using some (not all) samples.
    Type of Medium: Electronic Resource
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