Root mean square error criterion using operational deflection shape curvature for structural damage detection

Detection of structural damages using the vibration test data has been investigated by many researchers in the last decades. Identification of the defect in the early stages can prevent the unpredicted failure of structure. For beam-like structures, curvature techniques, e.g., mode shape curvature and flexibility curvature have been applied to localize the damage. In this paper, root mean square error using operational deflection shape curvature is used for damage detection. Differential Quadrature Method (DQM) is implemented to obtain the curvatures of the operational deflection shape. To demonstrate the effectiveness of method, the finite element model of a cantilever beam is used. The model is excited and the responses are measured in the simulated test. Enhanced frequency domain decomposition (EFDD) is used to extract the modal parameters. Also Mass change method is used to scale the mode shapes through which the scaled ODS parameters are computed. Using the RMSE the location of the damage is identified. The results show that this approach is more effective than the previous method

An Investigation into Different Power Consumption Parameters of Rushton Turbines: A Computational Survey

In the present work, the mixing process of shear thinning liquids in a six-blade Rushton turbine is studied. A finite volume based computational fluid dynamics (CFD) simulation has been carried out and the three-dimensional turbulent flow is numerically analyzed by using the Shear Stress Transport k-ω (k-ω SST) model. Shear thinning liquids were investigated and shear thinning behaviour was modelled by the Ostwald-de Waele law. The used stirred vessel has a cylindrical shape with a flat bottom and the liquid height was kept equal to the vessel diameter. Effects of the power law index and the angle of attack of the blade on power consumption have been investigated. The results show that decreasing the angle of attack from 90° to 45° not only results in an increase in the flow rate down to the bottom of the vessel, resulting in a better mixture qualification, but also reduces the power consumption of the stirring process. To verify the simulation, axial, radial and tangential velocity components were compared with other experimental data and satisfactory agreement was found

Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis

Rotary machines often exhibit nonlinear behavior due to factors such as nonlinear stiffness, damping, friction, coupling effects, and defects. Consequently, their vibration signals display nonlinear characteristics. Entropy techniques prove to be effective in detecting these nonlinear dynamic characteristics. Recently, an approach called fuzzy dispersion entropy (DE–FDE) was introduced to quantify the uncertainty of time series. FDE, rooted in dispersion patterns and fuzzy set theory, addresses the sensitivity of DE to its parameters. However, FDE does not adequately account for the presence of multiple time scales inherent in signals. To address this limitation, the concept of multiscale fuzzy dispersion entropy (MFDE) was developed to capture the dynamical variability of time series across various scales of complexity. Compared to multiscale DE (MDE), MFDE exhibits reduced sensitivity to noise and higher stability. In order to enhance the stability of MFDE, we propose a refined composite MFDE (RCMFDE). In comparison with MFDE, MDE, and RCMDE, RCMFDE’s performance is assessed using synthetic signals and three real bearing datasets. The results consistently demonstrate the superiority of RCMFDE in detecting various patterns within synthetic and real bearing fault data. Importantly, classifiers built upon RCMFDE achieve notably high accuracy values for bearing fault diagnosis applications, outperforming classifiers based on refined composite multiscale dispersion and sample entropy methods