182,541 research outputs found
Frisch scheme-based identification of multivariable errors-in-variables models
No full textThis paper describes an identification procedure for minimally parametrized multivariable models in the Errors–in–Variables (EIV) context of the Frisch scheme that considers additive white observation noise on the process inputs and outputs. This procedure relies on the geometric approach described in (Guidorzi and Diversi, 2009) that associates EIV models to directions in the noise space. The proposed procedure has been tested by means of a Monte Carlo simulation that confirms its effectiveness
A fast algorithm for errors-in-variables filtering
No full textThis paper concerns the optimal estimation of the input and output sequences of linear time-invariant errors-in-variables (EIV) processes. An efficient recursive filtering algorithm is proposed. It is an innovation-based approach that relies on the triangular decomposition of block Toeplitz matrices introduced in [1]. Unlike the other algorithms described in the literature, the proposed one is characterized by a computational complexity which increases only linearly with the order of the process. Both the SISO and MIMO cases are analyzed. An extension of the described algorithm to EIV models with colored input and output noises is considered as well
Minimal representations of MIMO time-varying systems and realization of cyclostationary models
No full textThis paper describes a family of minimally parameterized state-space models for completely n-step observable multi-input multi-output time-varying systems and their algebraical links with equivalent input-output models. These results are then used to develop an algorithm for the realization of generic input-output sequences generated by a periodic system. A complete numerical example is also developed to illustrate all steps of the algorithm described in the paper. (C) 2003 Elsevier Ltd. All rights reserved
A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring
No full textCondition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment’s state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time–frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time–frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura–Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark
Errors-in-Variables Identification of Composite Noncausal-FIR/IIR Models with Application to Transmissibility Identification
No full textModel structures used for system identification include infinite impulse response (IIR) models and finite impulse response (FIR) models. Identification using IIR models requires knowledge of the order of the system, where underestimating or overestimating the order of the system can yield poor parameter estimates. Although identification using FIR models does not require knowledge of the order of the system, FIR models cannot approximate systems with poles on or outside the unit circle. Noncausal FIR models can approximate systems with asymptotically stable and unstable poles, but not systems with poles on the unit circle. A composite noncausal-FIR/IIR (CNFI) model has an IIR part and a noncausal-FIR part, where the IIR part approximates poles on the unit circle, and the FIR part approximates the remaining part of the system. In this paper, we propose an errors-in-variables identification algorithm for CNFI models. We apply the proposed algorithm to identify transmissibilities, which are models that characterize the relationship between the outputs of an underlying system
RECURSIVE IDENTIFICATION OF NOISY AUTOREGRESSIVE MODELS VIA A NOISE-COMPENSATED OVERDETERMINED INSTRUMENTAL VARIABLE METHOD
No full textThe aim of this paper is to develop a new recursive identification algorithm for autoregressive (AR) models corrupted by additive white noise. The proposed approach relies on a set of both low-order and high-order Yule-Walker equations and on a modified version of the overdetermined recursive instrumental variable method, leading to the estimation of both the AR coefficients and the additive noise variance. The main motivation behind our proposition is introducing model identification procedures suitable for implementation on edge-computing platforms and programmable logic controllers (PLCs), which are known to have limited capabilities and resources when dealing with complex mathematical computations (i.e., matrix inversion). Indeed, our development is focused on condition monitoring systems, with particular attention paid to their integration onboard industrial machinery. The performance of the recursive approach is tested using both numerical simulations and a laboratory case study. The obtained results are very promising
Residual design for dynamic processes using de-coupling technique
No full textThe work presents some results concerning a fault detection scheme for dynamic processes using disturbance decoupling technique. The first step of the considered approach consists of exploiting input-output descriptions of the monitored system. In particular, the disturbance term of that model can be used to take into account unknown inputs affecting the system. The next step of the scheme leads to define a set of parity relations that can be used as residual signals since they are insensitive to the disturbance term. The proposed fault detection procedure has been tested on an industrial process simulator. Sensor and actuator faults have been simulated on a gas turbine model. Simulation results and concluding remarks have been finally reported
A bias-compensated identification approach for noisy FIR models
No full textA new bias-compensated least-squares method for identifying finite impulse response (FIR) models whose input and output are affected by additive white noise is proposed. By exploiting the statistical properties of the equation error of the noisy FIR system, an estimate of the input noise variance is obtained and the noise-induced bias is removed. The results obtained by means of Monte Carlo simulations show that the proposed algorithm outperforms other bias-compensated approaches and allows to obtain an estimation accuracy comparable to that of total least-squares without requiring the a priori knowledge of the input–output noise variance ratio
Residual generation and identification for dynamic processes
No full textThe work presents a preliminary study concerning fault detection for
dynamic processes using disturbance de-coupling technique. The first step of
the considered approach consists of exploiting input-output descriptions of the
monitored system. In particular, the disturbance term of that model can be used to
take into account unknown inputs affecting the system. The next step of the scheme
leads to the definition of basic parity relations that can be used as de-coupled
residual signals, as they are insensitive to the disturbance term. The proposed fault
detection procedure has been tested on an industrial pro. ~ss prototype. Sensor and
actuator faults have been simulated on a gas turbine model. Simulation results and
concluding remarks have been finally reported
EIV-based fault diagnosis in a light sport aircraft
No full textThis paper describes a fault detection procedure based on the analysis of the properties of the residuals of an Errors–in–Variables model. This procedure has been applied to the detection of flaps servo actuator faults on a light sport aircraft. The results obtained on real data collected in actual flight conditions have shown that this kind of fault and even its magnitude can be reliably detected by means of the proposed procedure
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