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The water-lubricated thrust bearings of the marine rim-driven thruster (RDT) are usually composed of polymer composites, which are prone to serious wear under harsh working conditions. Ultrasonic is an excellent non-destructive monitoring technology, but polymer materials are characterized by viscoelasticity, heterogeneity, and large acoustic attenuation, making it challenging to extract ultrasonic echo signals. Therefore, this paper proposes a wear monitoring method based on the amplitude spectrum of the ultrasonic reflection coefficient. The effects of bearing parameters, objective function, and algorithm parameters on the identification results are simulated and analyzed. Taking the correlation coefficient and root mean square error as the matching parameters, the thickness, sound velocity, density, and attenuation factor of the bearing are inversed simultaneously by utilizing the differential evolution algorithm (DEA), and the wear measurement system is constructed. In order to verify the identification accuracy of this method, an accelerated wear test under heavy load was executed on a multi-functional vertical water lubrication test rig with poly-ether-ether-ketone (PEEK) fixed pad and stainless-steel thrust collar as the object. The thickness of pad was measured using the high-precision spiral micrometer and ultrasonic testing system, respectively. Ultimately, the results demonstrate that the thickness identification error of this method is approximately 1%, and in-situmonitoring ability will be realized in the future, which is of great significance to the life prediction of bearings.

AbstractInformation fusion methods based on Dempster-Shafer evidence theory (DST) have been widely used in fault diagnosis. In DST-based methods, the monitoring information collected from sensors is modeled as multiple pieces of diagnosis evidence in the form of basic belief assignment (BBA), and Dempster’s rule is then used to combine these BBAs to obtain the fused BBA for diagnosis decision making. However, the belief structure with crisp singlevalued belief degrees in BBA may be too coarse to truthfully represent detailed fault information. Moreover, Dempster’s rule only uses a static combination process, which is unsuitable for dynamically fusing information collected at different time steps. In order to address these issues, the paper proposes a dynamic diagnosis method based on interval-valued evidential updating. First of all, the diagnosis evidence is constructed as an interval-valued belief structure (IBS), which provides a more informative scheme than BBA to model fault information. Secondly, the proposed evidential updating strategy can generate updated IBS as global diagnosis evidence by updating the previous evidence with the new incoming evidence recursively. Thirdly, the reliability and sensitivity indices are designed to evaluate and compare the performance of the proposed updating strategy with other commonly used strategies. Finally, the effectiveness of the proposed evidential updating strategy is demonstrated through some typical fault experiments of a machine rotor.

Information fusion methods based on Dempster-Shafer evidence theory (DST) have been widely used in fault diagnosis. In DST-based methods, the monitoring information collected from sensors is modeled as multiple pieces of diagnosis evidence in the form of basic belief assignment (BBA), and Dempster’s rule is then used to combine these BBAs to obtain the fused BBA for diagnosis decision making. However, the belief structure with crisp single-valued belief degrees in BBA may be too coarse to truthfully represent detailed fault information. Moreover, Dempster’s rule only uses a static combination process, which is unsuitable for dynamically fusing information collected at different time steps. In order to address these issues, the paper proposes a dynamic diagnosis method based on interval-valued evidential updating. First of all, the diagnosis evidence is constructed as an interval-valued belief structure (IBS), which provides a more informative scheme than BBA to model fault information. Secondly, the proposed evidential updating strategy can generate updated IBS as global diagnosis evidence by updating the previous evidence with the new incoming evidence recursively. Thirdly, the reliability and sensitivity indices are designed to evaluate and compare the performance of the proposed updating strategy with other commonly used strategies. Finally, the effectiveness of the proposed evidential updating strategy is demonstrated through some typical fault experiments of a machine rotor.

Studies have shown that both cystatin C and metabolic syndrome (MetS) are associated with inflammation. We set out to investigate the correlation between serum cystatin C levels and MetS in the elderly.