Abstracts:To better suppress the structural vibration in the micro vibration and harsh environment, anew typeofdamper, tuned particle damper (TPD), was designed by combining the advantage of classical dynamic vibration absorber (DVA) and particle damper (PD). An equivalent theoretical model was established to describe the dynamic behavior of a cantilever system treated with TPD. By means of a series of sine sweep tests, the dynamic characteristic of TPD under different excitation intensity was explored and the damping performance of TPD was investigated by comparing with classical DVA and PD with the same mass ratio. Experimental results show that with the increasing of excitation intensity TPD shows two different dynamic characteristics successively, i.e., PD-like and DVA-like. TPD shows a wider suppression frequency band than classical DVA and better practicability than PD in the micro vibration environment. Moreover, to characterize the dynamic characteristic of TPD, a simple evaluation of the equivalent dynamic mass and equivalent dynamic damping of the cantilever system treated with TPD was performed by fitting the experimental data to the presented theoretical model. Finally, based on the rheology behaviors of damping particles reported by the previous research results, an approximate phase diagram which shows the motion states of damping particles in TPD was employed to analyze the dynamic characteristic of TPD and several motion states of damping particles in TPD were presented via a high-speed camera.

Abstracts:In this study, the number, location and depth of cracks created in several Euler-Bernoulli beams, such as a simple beam and a more complex multi-step beam are investigated. The location and depth of the created cracks are determined using the hybrid Cuckoo-Nelder-Mead Optimization Algorithm (COA-NM) with high accuracy. The natural frequencies of the cracked beams are determined by solving frequency response equations, and performing modal test experiments. Results of COA-NM show a higher accuracy and convergence speed compared with other methods such as GA-NM, PSO-NM, GA, PSO, COA and several previous studies. Amount of calculations performed by COA-NM to achieve this accuracy is much less compared to other methods.

Abstracts:Angular displacement sensor on the actuator of automated manual transmission (AMT) is sensitive to fault, and the sensor fault will disturb its normal control, which affects the entire gear-shifting process of AMT and results in awful riding comfort. In order to solve this problem, this paper proposes a method of fault-tolerant control for AMT gear-shifting engaging process. By using the measured current of actuator motor and angular displacement of actuator, the gear-shifting engaging load torque table is built and updated before the occurrence of the sensor fault. Meanwhile, residual between estimated and measured angular displacements is used to detect the sensor fault. Once the residual exceeds a determined fault threshold, the sensor fault is detected. Then, switch control is triggered, and the current observer and load torque table estimates an actual gear-shifting position to replace the measured one to continue controlling the gear-shifting process. Numerical and experiment tests are carried out to evaluate the reliability and feasibility of proposed methods, and the results show that the performance of estimation and control is satisfactory.

Abstracts:Major physical systems/structures suffer from unwanted vibrations. For efficient working of such systems, these vibrations have to be controlled. In this paper, mathematical modeling of an aluminum cantilever beam with bonded multiple piezoelectric patches which act as the disturbance generator, sensor as well as control actuator has been presented. This piezoelectric laminate cantilever beam is assumed to be vibrating in a single degree of freedom i.e. in the flexural mode only and the corresponding state space models have been derived analytically using the finite element technique. Dominant modes of flexural vibration are identified from the frequency response of the developed model of the system and finally a state feedback controller based on pole placement technique is designed to actively suppress the vibrations. Through numerous simulations as well as experimental validation, the effectiveness of the active controller in damping the vibrations at various excitation frequencies as well as frequency ranges along the flexural mode is established.

Abstracts:This paper presents experimental and theoretical analyses of the second harmonic generation due to non-linear interaction of Lamb waves with a fatigue crack. Three-dimensional (3D) finite element (FE) simulations and experimental studies are carried out to provide physical insight into the mechanism of second harmonic generation. The results demonstrate that the 3D FE simulations can provide a reasonable prediction on the second harmonic generated due to the contact nonlinearity at the fatigue crack. The effect of the wave modes on the second harmonic generation is also investigated in detail. It is found that the magnitude of the second harmonic induced by the interaction of the fundamental symmetric mode (S0) of Lamb wave with the fatigue crack is much higher than that by the fundamental anti-symmetric mode (A0) of Lamb wave. In addition, a series of parametric studies using 3D FE simulations are conducted to investigate the effect of the fatigue crack length to incident wave wavelength ratio, and the influence of the excitation frequency on the second harmonic generation. The outcomes show that the magnitude and directivity pattern of the generated second harmonic depend on the fatigue crack length to incident wave wavelength ratio as well as the ratio of S0 to A0 incident Lamb wave amplitude. In summary, the findings of this study can further advance the use of second harmonic generation in damage detection.

Abstracts:This paper reports the design, analysis and testing of a parallel two degree-of-freedom piezo-actuated compliant stage for XY nanopositioning by introducing an innovative hybrid actuation mechanism. It mainly features the combination of two Scott–Russell and a half-bridge mechanisms for double-stage displacement amplification as well as moving direction modulation. By adopting the leaf-type double parallelogram (LTDP) structures at both input and output ends of the hybrid mechanism, the lateral stiffness and dynamic characteristics are significantly improved while the parasitic motions are greatly eliminated. The XY nanopositioning stage is constructed with two orthogonally configured hybrid mechanisms along with the LTDP mechanisms for totally decoupled kinematics at both input and output ends. An analytical model was established to describe the complete elastic deformation behavior of the stage, with further verification through the finite element simulation. Finally, experiments were implemented to comprehensively evaluate both the static and dynamic performances of the proposed stage. Closed-loop control of the piezoelectric actuators (PEA) by integrating strain gauges was also conducted to effectively eliminate the nonlinear hysteresis of the stage.

Abstracts:The performance of velocity & displacement-dependent damper (VDD), inspired by the semi-active control, is analyzed. The main differences among passive, displacement-dependent and semi-active dampers are compared on their damping properties. Valve assemblies of VDD are modelled to get an insight into its working principle. The mechanical structure composed by four valve assemblies helps to enable VDD to approach the performance by those semi-active control dampers. The valve structure parameters are determined by the suggested two-step process. Hydraulic model of the damper is built with AMEsim. Simulation result of F-V curves, which is similar to those of semi-active control damper, demonstrates that VDD could achieve the similar performance of semi-active control damper. The performance of a quarter vehicle model employing VDD is analyzed and compared with semi-active suspension. Simulation results show that VDD could perform as good as a semi-active control damper. In addition, no add-on hardware or energy consumption is needed for VDD to achieve the remarkable performance.

Abstracts:Gear fault diagnosis relies heavily on the scrutiny of vibration responses measured. In reality, gear vibration signals are noisy and dominated by meshing frequencies as well as their harmonics, which oftentimes overlay the fault related components. Moreover, many gear transmission systems, e.g., those in wind turbines, constantly operate under non-stationary conditions. To reduce the influences of non-synchronous components and noise, a fault signature enhancement method that is built upon angle-frequency domain synchronous averaging is developed in this paper. Instead of being averaged in the time domain, the signals are processed in the angle-frequency domain to solve the issue of phase shifts between signal segments due to uncertainties caused by clearances, input disturbances, and sampling errors, etc. The enhanced results are then analyzed through feature extraction algorithms to identify the most distinct features for fault classification and identification. Specifically, Kernel Principal Component Analysis (KPCA) targeting at nonlinearity, Multilinear Principal Component Analysis (MPCA) targeting at high dimensionality, and Locally Linear Embedding (LLE) targeting at local similarity among the enhanced data are employed and compared to yield insights. Numerical and experimental investigations are performed, and the results reveal the effectiveness of angle-frequency domain synchronous averaging in enabling feature extraction and classification.

Abstracts:In actual fault diagnosis, useful information is often submerged in heavy noise, and the feature information is difficult to extract. Traditional methods, such like stochastic resonance (SR), which using noise to enhance weak signals instead of suppressing noise, failed in chaotic background. Neural network, which use reference sequence to estimate and reconstruct the background noise, failed in white Gaussian noise. To solve these problems, a novel weak signal detection method aimed at the problem of detecting impact signal buried under heavy chaotic and Gaussian background noise is proposed. First, the proposed method obtains the virtual reference sequence by constructing the Hankel data matrix. Then an M-order optimal FIR filter is designed, which can minimize the output power of background noise and pass the weak periodic signal undistorted. Finally, detection and reconstruction of the weak periodic signal are achieved from the output SBNR (signal to background noise ratio). The simulation shows, compared with the stochastic resonance (SR) method, the proposed method can detect the weak periodic signal in chaotic noise background while stochastic resonance (SR) method cannot. Compared with the neural network method, (a) the proposed method does not need a reference sequence while neural network method needs one; (b) the proposed method can detect the weak periodic signal in white Gaussian noise background while the neural network method fails, in chaotic noise background, the proposed method can detect the weak periodic signal under a lower SBNR (about 8–17dB lower) than the neural network method; (c) the proposed method can reconstruct the weak periodic signal precisely.

Abstracts:Linear Brownian motion with constant drift is widely used in remaining useful life predictions because its first hitting time follows the inverse Gaussian distribution. State space modelling of linear Brownian motion was proposed to make the drift coefficient adaptive and incorporate on-line measurements into the first hitting time distribution. Here, the drift coefficient followed the Gaussian distribution, and it was iteratively estimated by using Kalman filtering once a new measurement was available. Then, to model nonlinear degradation, linear Brownian motion with adaptive drift was extended to nonlinear Brownian motion with adaptive drift. However, in previous studies, an underlying assumption used in the state space modelling was that in the update phase of Kalman filtering, the predicted drift coefficient at the current time exactly equalled the posterior drift coefficient estimated at the previous time, which caused a contradiction with the predicted drift coefficient evolution driven by an additive Gaussian process noise. In this paper, to alleviate such an underlying assumption, a new state space model is constructed. As a result, in the update phase of Kalman filtering, the predicted drift coefficient at the current time evolves from the posterior drift coefficient at the previous time. Moreover, the optimal Kalman filtering gain for iteratively estimating the posterior drift coefficient at any time is mathematically derived. A discussion that theoretically explains the main reasons why the constructed state space model can result in high remaining useful life prediction accuracies is provided. Finally, the proposed state space model and its associated Kalman filtering gain are applied to battery prognostics.