Personalized medicine is a hot topic to develop a medical procedure for healthcare. Motivated by molecular dynamics simulation-based personalized medicine, we propose a novel numerical simulation-based personalized diagnosis methodology and explain the fundamental procedures. As an example, a personalized fault diagnosis method is developed using the finite element method (FEM), wavelet packet transform (WPT) and support vector machine (SVM) to detect faults in a shaft. The shaft unbalance, misalignment, rub-impact and the combination of rub-impact and unbalance are investigated using the present method. The method includes three steps. In the first step, Theil’s inequality coefficient (TIC)-based FE model updating technique is employed to determine the boundary conditions, and the fault-induced FE model of the faulty shaft is constructed. Further, the vibration signals of the faulty shaft are obtained using numerical simulation. In the second step, WPT is employed to decompose the vibration signal into several signal components. Specific time-domain feature parameters of all of the signal components are calculated to generate the training samples to train the SVM. Finally, the measured vibration signal and its components decomposed by WPT serve as a test sample to the trained SVM. The fault types are finally determined. In the simulation of a simple shaft, the classification accuracy rates of unbalance, misalignment, rub-impact and the combination of rub-impact and unbalance are 93%, 95%, 89% and 91%, respectively, whereas in the experimental investigations, these decreased to 82%, 87%, 73% and 79%. In order to increase the fault diagnosis precision and general applicability, further works are continuously improving the personalized diagnosis methodology and the corresponding specific methods.

Piezoelectric sensors array based damage imaging method as a high resolution source localization algorithm is becoming a promising method in structural health monitoring (SHM) technology. However, the environmental variations could affect the gain-phase of array signal. This paper experimentally evaluates the environmental effects on piezoelectric sensors array, and presents a compensated 2D-MUSIC based damage imaging method for composite structures. Firstly, detailed analysis and comparison discussion about the gain-phase difference of array signal when the environmental parameters change, and the gain-phase changes respect to the environmental parameters could be obtained. Secondly, array error matrix is structured and substituted into the steering vector of the original 2D-MUSIC algorithm to compensate. Finally, the compensated 2D-MUSIC algorithm is applied for estimating the initial estimates of damage. After substituting these initial estimates, the cost function is minimized by adaptive iterative calculating the reasonable location of the damage source. The experiments on an epoxy laminate plate demonstrate the validity and effectiveness of the proposed method.

Multiple signal classification (MUSIC) algorithm-based structural health monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, in previous MUSIC-based impact location methods, the narrowband signals at a particular central frequency had to be extracted from the wideband Lamb waves induced by each impact using a wavelet transform. Additionally, the specific center frequency had to be obtained after carefully analyzing the impact signal, which is time consuming. Aiming at solving this problem, this paper presents an improved approach that combines the optimized ensemble empirical mode decomposition (EEMD) and two-dimensional multiple signal classification (2D-MUSIC) algorithm for real-time impact localization on composite structures. Firstly, the impact signal at an unknown position is obtained using a unified linear sensor array. Secondly, the fast Hilbert Huang transform (HHT) with an optimized EEMD algorithm is introduced to extract intrinsic mode functions (IMFs) from impact signals. Then, all IMFs in the whole frequency domain are directly used as the input vector of the 2D-MUSIC model separately to locate the impact source. Experimental data collected from a cross-ply glass fiber reinforced composite plate are used to validate the proposed approach. The results show that the use of optimized EEMD and 2D-MUSIC is suitable for real-time impact localization of composite structures.

A wavelet-based finite element method (WFEM) is developed to calculate the elastic band structures of two-dimensional phononic crystals (2DPCs), which are composed of square lattices of solid cuboids in a solid matrix. In a unit cell, a new model of band-gap calculation of 2DPCs is constructed using plane elastomechanical elements based on a B-spline wavelet on the interval (BSWI). Substituting the periodic boundary conditions (BCs) and interface conditions, a linear eigenvalue problem dependent on the Bloch wave vector is derived. Numerical examples show that the proposed method performs well for band structure problems when compared with those calculated by traditional FEM. This study also illustrates that filling fractions, material parameters, and incline angles of a 2DPC structure can cause band-gap width and location changes.

Gains and phases delay induced by sensor position error would significantly degrade the performance of high-resolution two-dimensional multiple signal classification (2D-MUSIC) algorithm, which resulting in low positioning estimation accuracy and poor imaging. In this study, adaptive piezoelectric sensor array calibration based method is proposed for impact localization on composite structure. First, observed signal vector from the sensor array is represented by error calibration matrix with unknown gains and phases, and then it used to construct the cost function including sensor array parameters. Second, a 2D-MUSIC algorithm based on linear attenuation calibration is applied for estimating the initial estimate of impact location. Finally, substituting the initial estimate, the cost function is minimized by adaptive iterative to calculate the sensor array error parameters and the exact location of the impact source. Both finite element method (FEM) simulation and experimental results on carbon-fiber composite panel demonstrate the validity and effectiveness of the proposed method.

As an impact source localization technique, Lamb waves are commonly used to detect low velocity impacts in composite structures. However, the performance of Lamb waves is susceptible to vibration conditions. In this paper, vibration effects on Lamb waves are explored, and an adaptive-filtering enhanced multiple signal classification (AF-MUSIC) method is proposed to generate a relative robust impact source localization method. The response sensor array signals are contaminated by noises, which makes the traditional multiple signal classification (MUSIC) method unable to localize impact sources. In present method, the adaptive-filtering is utilized to reduce vibration induced noises of array data, then the impact position represented by the angle and distance in polar coordinates are finally estimated by scanning the whole monitoring area using MUSIC. The experimental results show that the present method can relatively robust localizes impact sources effectively, especially for the vibration conditions.