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Highlights • This paper focuses on the implementation of motion control for active suspension systems using a novel scheme of model-free finite-time tracking control method. • The proposed strategy facilitates the realization since the system plant modeling is not needed. • Unlike the existing control methods, the proposed disturbance compensator is continuous and finite-time convergent, which shows a great potential in application to practical suspension systems. • The advantage of the presented control algorithm is verified via comparative investigation, especially the comparison of the existing extended state observer-based feedback control scheme. • Comparative experimental studies are presented to confirm the effectiveness and superiority of the proposed control strategy over the traditional methods. Abstract This paper focuses on the implementation of motion control for active suspension systems using a novel scheme of model-free finite-time tracking control method. The proposed strategy facilitates the realization since the system plant modeling is not needed. Correspondingly, the suspension vertical dynamics including external disturbances are estimated by the time-delay estimation, and its estimated error is compensated by the integral sliding mode control scheme. Theoretical analysis proves that the sliding mode and desired system dynamics can achieve a finite time convergence performance with continuous control law. In active suspension control, the continuous control law contributes to preventing the unexpected chattering in practical implementation. The advantage of the presented control algorithm is verified via comparative investigation, especially the comparison of the existing extended state observer-based feedback control scheme, which requires a high-gain observer to realize the desired dynamics. Comparative experimental studies are presented to confirm the effectiveness and superiority of the proposed control strategy over the traditional methods. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a novel fuzzy system based method for modelling both rate-independent and rate-dependent hysteresis in the piezoelectric actuator is proposed. First, the partial Takagi–Sugeno (T–S) fuzzy rule is designed. The antecedent structure of the fuzzy system is identified through uniform partition of its input variable. Then, the parameters of the consequent structure are optimized via the recursive least squares (RLS) algorithm. The modelling method is simple to implement and highly efficient to compute. Experimental results show that the proposed method is efficient to model both rate-independent and rate-dependent hysteresis. Based on the inverse of the developed model, feed-forward hysteresis compensation experiments at the frequencies of 50Hz and 100Hz are also conducted with the hysteresis effects being obviously reduced. The major contribution of this paper is that the inverse of the model can be analytically computed and the method can be applied to the case of real-time on-line modelling. [Copyright &y& Elsevier]

The model-free hysteresis loop analysis (HLA) method for structural health monitoring (SHM) has significant advantages over the traditional model-based SHM methods that require a suitable baseline model to represent the actual system response. This paper provides a unique validation against both an experimental reinforced concrete (RC) building and a calibrated numerical model to delineate the capability of the model-free HLA method and the adaptive least mean squares (LMS) model-based method in detecting, localizing and quantifying damage that may not be visible, observable in overall structural response. Results clearly show the model-free HLA method is capable of adapting to changes in how structures transfer load or demand across structural elements over time and multiple events of different size. However, the adaptive LMS model-based method presented an image of greater spread of lesser damage over time and story when the baseline model is not well defined. Finally, the two algorithms are tested over a simpler hysteretic behaviour typical steel structure to quantify the impact of model mismatch between the baseline model used for identification and the actual response. The overall results highlight the need for model-based methods to have an appropriate model that can capture the observed response, in order to yield accurate results, even in small events where the structure remains linear. [ABSTRACT FROM AUTHOR]

Severe blade vibrations may reduce the useful life of the high-speed blade. Nowadays, non-contact measurement using blade tip-timing (BTT) technology is becoming promising in blade vibration monitoring. However, blade tip-timing signals are typically under-sampled. How to extract characteristic features of unknown multi-mode blade vibrations by analyzing these under-sampled signals becomes a big challenge. In this paper, a novel BTT analysis method for reconstructing unknown multi-mode blade vibration signals is proposed. The method consists of two key steps. First, a sparse representation (SR) mathematical model for sparse blade tip-timing signals is built. Second, a multi-mode blade vibration reconstruction algorithm is proposed to solve this SR problem. Experiments are carried out to validate the feasibility of the proposed method. The main advantage of this method is its ability to reconstruct unknown multi-mode blade vibration signals with high accuracy. The minimal requirements of probe number are also presented to provide guidelines for BTT system design. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the Expectation Maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used. [Copyright &y& Elsevier]

Abstract: An algorithm for detecting cracks on the beams and a statistical process for minimising the measurement errors in experiments are presented in this paper. Natural frequencies are determined by using the theoretical model for different depths and locations of single crack. The ratios of cracked and un-cracked beam’s natural frequencies constitute the prediction tables scaled in two axes as crack location and crack depth. Frequency contour lines corresponding to measured natural frequency ratios are matched with the interpolated prediction table, called frequency map, and are used for detection of a single crack. However, contour lines do not give any information about the existence of two cracks. The algorithm presented in this paper makes it possible to locate the suitable positions of two cracks searched over the frequency map. The algorithm is tested in the examples employing the frequency map prepared by the theory presented and the input frequency ratios obtained by the commercial finite element program. The algorithm is also verified by using the natural frequencies of cracked and un-cracked cantilever beams employed in several experiments. In measurement, determination of accurate natural frequency ratios is crucial for the success of crack detection. Therefore, this paper also presents a statistical approach called ‘recursively scaled zoomed frequencies (RSZF)’ for minimising the deviations caused by sensitivity and resolution lack in measured natural frequencies. In this approach, the measured frequencies in spectrum are modified by the mean value of the natural frequencies determined in different frequency scales. Zoomed frequencies are obtained by the cubic spline interpolation method that increases the resolution of frequency spectrum. RSZF comes into further prominence especially when the cracks are needed to be detected by very small sized data. All of the experimental results represent that single crack and double cracks are successfully detected by using the methods presented. [Copyright &y& Elsevier]

The matrix model updating problem (MMUP), considered in this paper, concerns updating a symmetric second-order finite element model so that the updated model reproduces a given set of desired eigenvalues and eigenvectors by replacing the corresponding ones from the original model, and preserves the symmetry of the original model. In an optimization setting, this is a constrained nonlinear optimization problem. Taking advantage of the special structure of the constraint sets, it is first shown that the MMUP can be formulated as an optimization problem over the intersection of some special subspaces and linear varieties on the space of matrices. Using this formulation, an alternating projection method (APM) is then proposed and analyzed. The projections onto the involved subspaces and linear varieties are characterized. To the best of our knowledge, an alternating projection method for MMUP has not been proposed in the literature earlier. A distinct practical feature of the proposed method is that it is implementable using only a few measured eigenvalues and eigenvectors. No knowledge of the eigenvalues and eigenvectors of the associated quadratic matrix pencil is required. The results of our numerical experiments on both illustrative and benchmark problems show that the algorithm works well. The paper concludes with some future research problems. [Copyright &y& Elsevier]

Abstract: The focus of this paper is Bayesian modal parameter recursive estimation based on an interacting Kalman filter algorithm with decoupled distributions for frequency and damping. Interacting Kalman filter is a combination of two widely used Bayesian estimation methods: the particle filter and the Kalman filter. Some sensitivity analysis techniques are also proposed in order to deduce a recursive estimate of modal parameters from the estimates of the damping/stiffness coefficients. [Copyright &y& Elsevier]

Abstract: The cylinder pressure is arguably the most important variable characterizing the combustion process in internal combustion engines. In light of the recent advances in combustion technologies and in engine control, the use of cylinder pressure is now frequently considered as a feedback signal for closed-loop combustion control algorithms. In order to generate an accurate pressure trace for real-time combustion control and diagnostics, the output of the in-cylinder pressure transducer must be conditioned with signal processing methods to mitigate the well-known issues of offset and noise. While several techniques have been proposed for processing the cylinder pressure signal with limited computational burden, most of the available methods still require one to apply low-pass filters or moving average windows in order to mitigate the noise. This ultimately limits the opportunity of exploiting the in-cylinder pressure feedback for a cycle-by-cycle control of the combustion process. To this extent, this paper presents an estimation algorithm that extracts the pressure signal from the in-cylinder sensor in real-time, allowing for estimating the 50% burn rate location and IMEP on a cycle-by-cycle basis. The proposed approach relies on a model-based estimation algorithm whose starting point is a crank-angle based engine combustion model that predicts the in-cylinder pressure from the definition of a burn rate function. Linear parameter varying (LPV) techniques are then used to expand the region of estimation to cover the engine operating map, as well as allowing for real-time cylinder estimation during transients. The estimator is tested on the experimental data collected on an engine dynamometer as well as on a high-fidelity engine simulator. The results obtained show the effectiveness of the estimator in reconstructing the cylinder pressure on a crank-angle basis and in rejecting measurement noise and modeling errors, with considerably low computation effort. [Copyright &y& Elsevier]

Abstract: In this paper, an innovative algorithm of improved EMD (IEMD) has been presented for the analysis of frequency modulation (FM) signals. First, extract the first level extrema and the second level extrema to construct one two-level extrema structure (TLES). Using the locations'' relations between the first level extrema and the second level extrema, what the traditional EMD does for FM signals is determined. The propositions that can help us to judge whether the multi-component can be separated well or not by EMD in practice and then tell us what the EMD will do in practice are deduced. Four cases are presented for decomposition of FM components using EMD. According to these cases, different improved methods are presented to separate the FM components that EMD fails to separate. The least-squares method is used to estimate the phase polynomials of FM components. The orthogonal relations between different components are used to estimate the optimal amplitudes of FM components. Moreover, the performance of EMD is analyzed via the multi-level extrema distribution as well, which can be used in practice for the aforehand judgement. Finally, experiments are given to show our new improved EMD. [Copyright &y& Elsevier]

Abstract: In this paper, consistent multiple-input frequency-domain estimators will be presented based on a right matrix-fraction description of the frequency response function. This matrix-fraction description leads to a fast algorithm in the same way as the least-squares complex frequency-domain estimator (LSCF). The use of multiple inputs simultaneously in the estimation (so-called polyreference estimation) has the advantage that it allows to separate closely spaced modes. The main drawback of the LSCF estimator is that it is theoretically inconsistent, i.e.the estimates do not converge to the true values when the number of measurements increases to infinity. However, the noise information available from most measurements can be used to construct a maximum likelihood-like weighting for the LSCF estimators, giving consistent estimates. The results are fast, polyreference, and consistent weighted generalised total-least-squares (WGTLS) estimators. The iterative quadratic maximum likelihood (IQML) estimator is practically consistent for high signal-to-noise ratios, with the additional advantage that it yields clear stabilisation charts. The performance of the presented WGTLS and IQML estimators is evaluated by means of ground vibration test data and demonstrated on flight flutter test data. [Copyright &y& Elsevier]

Abstract: Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms. [Copyright &y& Elsevier]

Abstract: In this paper, it is shown how a strategy based on algorithms for computing cuts in undirected networks, can be applied to reduce energy transmission in realistic statistical energy analysis (SEA) models, with the sole modification of a limited number of internal and coupling loss factors. The frequency dependent case of SEA systems with multiple sources and targets is considered. A mathematical justification for the strategy is also provided, which relies on an analysis of the series expansion of the energy vector, in terms of the powers of the SEA graph adjacency matrix. A numerical example of vibroacoustic transmission in a simple test building has been included to show the performance of the approach. [Copyright &y& Elsevier]

Abstract: Non-Gaussian random shaker testing with kurtosis control is introduced in the paper as a way of increasing or decreasing the excitation crest factor (CF). The CF increase is required for more accurate simulation of ground vehicle vibrations and the CF decrease is useful in other applications such as modal testing. Using kurtosis as a measure of CF behavior leads to closed-form solution for making the IFFT generation non-Gaussian by special phase manipulation. A universal phase selection procedure capable of modeling random excitations with a high or low kurtosis has been developed. Because of the analytical solution advantage, the proposed phase method meets time restrictions critical for shaker controller operation. Low CF values achieved by the suggested analytical kurtosis-based solution are close enough to those obtained by the known method of sequential moment minimization, which considers moments of higher order but by numerical optimization only. In the case of CF increase, the developed phase manipulation algorithm randomizes high peaks in terms of their amplitude, position, and the number of severe peaks per data block. This ensures realistic variability of high peak behavior distinct from having just one high peak of narrow height variation in all data blocks as in another known approach of on-band phase limiting that is also a numerical technique, not an analytical solution as the proposed method. [Copyright &y& Elsevier]

Abstract: A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input–output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach. [Copyright &y& Elsevier]

Abstract: This paper considers the problem of finite element model (FEM) updating in the context of model selection. The FEM updating problem arises from the need to update the initial FE model that does not match the measured real system outputs. This inverse system identification-problem is made even more complex by the uncertainties in modeling some of the structural parameters. Such uncertainty often results in a number of competing forms of FE models being proposed which leads to lack of consensus in the field. A model can be formulated in a number of ways; by the number, the location and the form of the updating parameters. We propose the use of a Bayesian evidence statistic to help decide on the best model from any given set of models. This statistic uses the recently developed stochastic nested sampling algorithm whose by-product is the posterior samples of the updated model parameters. Two examples of real structures are each modeled by a number of competing finite element models. The individual model evidences are compared using the Bayes factor, which is the ratio of evidences. Jeffrey''s scale is then used to determine the significance of the model differences obtained through the Bayes factor. [Copyright &y& Elsevier]

Abstract: In this paper a sensorless speed detection method of induction squirrel-cage machines is presented. This method is based on frequency determination of the stator neutral point voltage primary slot harmonic, which is dependent on rotor speed. In order to prove method in steady state and dynamic conditions the simulation and experimental study was carried out. For theoretical investigation the mathematical model of squirrel cage induction machines, which takes into consideration actual geometry and windings layout, is used. Speed-related harmonics that arise from rotor slotting are analyzed using digital signal processing and DFT algorithm with Hanning window. The performance of the method is demonstrated over a wide range of load conditions. [Copyright &y& Elsevier]

Abstract: Most existed LMS algorithms have noisy gradient estimate since they use instantaneous square error to estimate ensemble average of the square errors. In this paper, a new type of LMS algorithm is developed for active noise control. The gradient estimate in the proposed algorithm is improved by using the weighted time average instead of the instantaneous value to approximate the ensemble average. Compared to the filtered-x LMS algorithm, the proposed algorithm is shown to have many improved convergence properties. Computer simulations are given to support the theoretical analysis. [Copyright &y& Elsevier]