Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.     

Spectroscopic properties of the low-lying electronic states and laser cooling feasibility for the SrI molecule

Laser cooling of a molecule with heavy nuclei is often complicated because of the density distribution of the electronic states. Here, we evaluate the feasibility of the laser cooling of the SrI molecule by calculating the potential energy curves and transition dipole moments of the ground and low-lying excited states using the multi-reference configuration interaction plus Davidson corrections (MRCI + Q) and the all-electron basis sets of ANO-RCC. The relativistic effect and the spin-orbit coupling splits are included, because both Sr and I are heavy atoms. Based on the obtained potential energy curves, we solve the Schrödinger equation of nuclear motion to determine the rovibrational energy levels and the Franck-Condon factors. The spectroscopic parameters are obtained by fitting the rovibrational energy levels with the Dunham expression. The radiation lifetimes, the Doppler and recoil temperatures between the X²Σ⁺ and the ²Π_1/2/²Π_3/2/B²Σ⁺ states are calculated. 5-color laser cooling schemes for the molecule are proposed, which can lead to the total effective Franck-Condon factors being 0.99983, 0.99979, and 0.99941 for the three transitions, respectively. All the obtained results suggest that the SrI molecule is a feasible candidate for laser cooling.     

Investigation of Relationships between Grain Structure and Inhomogeneous Deformation by Means of Laboratory-Based Multimodal X-Ray Tomography: Strain Accuracy Analysis

Experimental Mechanics - The internal strain distribution developing during plastic deformation is important for understanding the mechanical properties of polycrystalline materials. Such...     

David Hull

Journal of Optimization Theory and Applications -     

Flow-induced shear stress and deformation of a core–shell-structured microcapsule in a microchannel

A numerical model was developed and validated to investigate the fluid–structure interactions between fully developed pipe flow and core–shell-structured microcapsule in a microchannel. Different flow rates and microcapsule shell thicknesses were considered. A sixth-order rotational symmetric distribution of von Mises stress over the microcapsule shell can be observed on the microcapsule with a thinner shell configuration, especially at higher flow rate conditions. It is also observed that when being carried along in a fully developed pipe flow, the microcapsule with a thinner shell tends to accumulate stress at a higher rate compared to that with a thicker shell. In general, for the same microcapsule configuration, higher flow velocity would induce a higher stress level over the microcapsule shell. The deformation gradient was used to capture the microcapsule's deformation in the present study. The effect of Young's modulus on the microcapsule shell on the microcapsule deformation was investigated as well. Our findings will shed light on the understanding of the stability of core–shell-structured microcapsule when subjected to flow-induced shear stress in a microfluidic system, enabling a more exquisite control over the breakup dynamics of drug-loaded microcapsule for biomedical applications.     

Nonlinear vibration response of a complex aeroengine under the rubbing fault

Nonlinear Dynamics - Rolling bearing and squeeze film damper will introduce structural nonlinearity into the dynamic model of aeroengine. Rubbing will occur due to the clearance reduction design of...     

Optimization of Transducer Location for Novel Non-Intrusive Methodologies of Diagnosis in Diesel Engines

The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects of changing various fuel injection parameters was analyzed. Scope of using non-intrusive technique has been analyzed by changing locations of microphone. Novelty of this worklies in exploring signal processing methods for various locations around the engine test set up. Various frequency ranges of contributing noise and vibration sources were identified. Time-Frequency analysis showed the onset of various cyclic. Based on the identification of various frequency bands, it is possible to device suitable filters in order to extract more information.     

Prediction of the drag reduction effect of pulsating pipe flow based on machine learning

Prediction of drag reduction effect caused by pulsating pipe flows is examined using machine learning. First, a large set of flow field data is obtained experimentally by measuring turbulent pipe flows with various pulsation patterns. Consequently, more than 7000 waveforms are applied, obtaining a maximum drag reduction rate and maximum energy saving rate of 38.6% and 31.4%, respectively. The results indicate that the pulsating flow effect can be characterized by the pulsation period and pressure gradient during acceleration and deceleration. Subsequently, two machine learning models are tested to predict the drag reduction rate. The results confirm that the machine learning model developed for predicting the time variation of the flow velocity and differential pressure with respect to the pump voltage can accurately predict the nonlinearity of pressure gradients. Therefore, using this model, the drag reduction effect can be estimated with high accuracy.     

Optimal parameter selection for constraint-following control for mechanical systems based on Stackelberg game

Nonlinear Dynamics - This paper proposes an optimal parameter design of control scheme for mechanical systems by adopting the Stackelberg game theory. The goal of the control is to drive the...