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An instantaneous frequency identification method of vibration signal based on linear chirplet transform and Wigner-Ville distribution is presented. This method has an obvious advantage in identifying closely spaced and time-varying frequencies. The matching pursuit algorithm is employed to select optimal chirplets, and a modified version of chirple...
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The ambiguity function (AF) is an essential time-frequency analysis tool to analyze the radar waveform properties in radar applications. It can be used effectively and reliably to analyze properties like the peak-to-side-lobe ratio, time delay resolution, Doppler resolution and tolerance characteristic. However, it fails to analyze higher-order chi...
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... In this thesis, a matching pursuit algorithm is used for extracting the signal reflected from the damage. Matching pursuit is used by several researchers for damage detection in structural health monitoring applications [54,64,[66][67][68][69][70][71]. A matching pursuit algorithm is required to extract the wave reflected from the damage [70,72]. ...
Adhesively bonded joints have been widely accepted and increasingly used in major loadcarrying structural components due to many advantages over classical mechanical fasteners, like riveting and bolting. Fatigue damage remains one of the primary causes of failure in adhesively bonded joints. In this work, probabilistic fatigue life prediction and probabilistic damage prognostics frameworks are developed for adhesively bonded joints. The probabilistic fatigue life prediction framework calibrates the fatigue life model by quantifying uncertainty in the fatigue damage evolution relation using experimental fatigue life data. Probabilistic assessment of fatigue life is simulated through damage evolution along the bondline and Bayesian inference via the Markov chain Monte Carlo (MCMC) sampling method for inverse uncertainty quantification (UQ) in fatigue model parameters. To expedite the fatigue life simulation, a hybrid model composed of physics-based fatigue damage evolution relation and a data-driven artificial neural networks (ANNs) model is employed. The degradation of the adhesive is evaluated by the fatigue damage evolution relation which is then mapped to the strain redistribution along the bondline using the ANNs model.
Once the mapping is learned by the ANNs, through data from FEA simulations, the probabilistic fatigue life prediction framework involves three successive modules: (I) fatigue damage growth (FDG) simulator, (II) inverse UQ in fatigue model parameters, and (III) confidence bounds for fatigue life prediction. The developed FDG simulator can be used for simulating fatigue degradation rapidly for a given geometric configuration under any arbitrary fatigue loading spectra. The quantified model parameter uncertainties from the framework correspond to the intrinsic statistical material properties that can be used for probabilistic fatigue life prediction in any joint configuration with the same adhesive material.
The probabilistic framework is verified using a single lap joint (SLJ) by quantifying uncertainties which are then used for probabilistic fatigue life prediction in laminated doublers in the bending (LDB) joint, that uses the same adhesive material as SLJ, and is successfully compared with experimental data. The framework is also tested and validated by estimating probabilistic fatigue life in other joint configurations under constant and variable amplitude fatigue loading spectra. The probabilistic damage prognostics framework is the integration of diagnostic method, hybrid FDG simulator, and uncertainty quantification method. In the diagnostic method, ultrasonic Lamb waves are excited into the structure using a pair of piezo wafers and the damage extent is quantified by reconstructing the reflected signal from the bond region. The proposed diagnostic technique is verified using the signal generated by the finite element simulations with various levels of damage. The diagnostic technique is applied intermittently to quantify the damage extent and the data is used to calibrate the FDG simulator for inversely quantifying parameter uncertainties. The integrated damage prognostics framework is verified by estimating RUL for adhesively bonded joint configurations under different loading conditions.
In the developed frameworks, the MCMC method is used for UQ and these methods are inherently serial. The UQ simulation takes hours of computational time even with the hybrid FDG simulator. To speed up the process, the sequential Monte Carlo (SMC) method is used to run UQ simulations on parallel processors. The SMC method is first demonstrated on the problem of sampling from high dimensional space and the parallelization capabilities of the SMC method is demonstrated by benchmarking the problem on different cloud architecture. Next, the MCMC method in the proibablistic life prediction frameworks is replaced using the SMC method, and parallel UQ simulations are conducted on the Henry2 cluster. A good speed-up of UQ simulations is achieved. Finally, a portable and scalable Raspberry Pi cluster is built and the framework is deployed on the cluster to test its compactness.
... For the frequency estimation of the signal, the commonly used methods include peak detection, Short Time Fourier Transform (STFT) [17], Wavelet Transform (WT) [18], Wigner-Ville Distribution (WVD) [19], Hilbert-Huang Transform (HHT) [20], Chirp Transform (CT) [21], Polynomial Chirp Transform (PCT) [22], Zoom FFT [23], and Chirp-Z Transform (CZT) [24]. Among them, the peak detection method calculates the signal period by detecting the peak value of the signal, thereby obtaining the frequency of the signal. ...
The accurate measurement of roll angular rate for high spinning projectile has long been a challenging problem. Aiming to obtain the accurate roll angular rate of high spinning projectile, a novel extraction and filter algorithm, BSCZT-KF, is proposed in this paper. Firstly, a compound angular motion model of high spinning projectile is established. According to the model, we translate the roll angular rate measurement problem into a frequency estimation problem. Then the improved CZT algorithm, BSCZT, was employed to realize an accurate estimation of the narrowband signal frequency. Combined with the peak detection method, the BSCZT-KF algorithm is presented to further enhance the frequency estimation accuracy and the real-time performance. Finally, two sets of actual flight tests were conducted to verify the effectiveness and accuracy of the algorithm. The test results show that the average error of estimated roll angular rate is about 0.095% of the maximum of roll angular rate. Compared with the existing methods, the BSCZT-KF has the highest frequency estimation accuracy for narrowband signal.
... However, there exists problems like loss of local signal amplitude information, huge computation and decomposition of low efficiency, and so on. Moreover, the researchers have combined sparse theory with the other theories and presented many new methods for fault diagnosis [27,28]. Wang et al. [29] combined minimum entropy solution of convolution with sparse decomposition for feature extraction of the weak fault. ...
The vibration signal measured from the mechanical equipment is associated with the operation of key structure, such as the rolling bearing and gear. The effective signal processing method for early weak fault has attracted much attention and it is of vital importance in mechanical fault monitoring and diagnosis. The recently proposed atomic sparse decomposition algorithm is performed around overcomplete dictionary instead of the traditional signal analysis method using orthogonal basis operator. This algorithm has been proved to be effective in extracting useful components from complex signal by reducing influence of background noises. In this paper, an improved linear frequency-modulated (Ilfm) function as an atom is employed in the proposed enhanced orthogonal matching pursuit (EOMP) algorithm. Then, quantum genetic algorithm (QGA) with the OMP algorithm is integrated since the QGA can quickly obtain the global optimal solution of multiple parameters for rapidly and accurately extracting fault characteristic information from the vibration signal. The proposed method in this paper is superior to the traditional OMP algorithm in terms of accuracy and reducing the computation time through analyzing the simulation data and real world data. The experimental results based on the application of gear and bearing fault diagnosis indicate that it is more effective than traditional method in extracting fault characteristic information.
The chapter devoted studying some characteristics of signal used in receiving devices by LoRa technology. The main feature of this technology relates the ability to build wireless networks for transmitting short messages over long distances under the condition of the long-life battery. Our investigation focused on the impact of interference on the functioning of the receiving set. With this purpose, we studied the basic characteristics of the received radio signal, its frequency properties, the possibilities of spreading the spectrum, encoding and recovering the transmitted information, receiving a packet of individual pulse signals and the quality of packet processing as a whole, and the effect of multipath propagation. We also proposed an approach for studying a receiver in highly populated areas with an estimate of the calculating and experimental data. To check the quality of processing the received information the studies were carried out according to the “point-to-point” and “point-gateway” schemes under the remote server. The test results follow.
