Presence Of Non-stationary Disturbances Research Articles

Uncertainty-based combination of signal processing techniques for the identification of rotor imbalance

This paper describes a method for the uncertainty-based combination of signal processing techniques for the identification of rotor imbalance. The main idea of the proposed method is to compute the imbalance with different algorithms and to average the different algorithms’ results. The method is based on the data fusion at feature level and uses the measurement uncertainty of the imbalance as a figure of merit for the weight computation. A static, a dynamic, and a hybrid implementation are presented. In the static one, the weights are computed in a dedicated training phase, in which four algorithms (Fourier transform and quasi-harmonic fitting of signal denoised with Hilbert-Huang Transform, Hilbert Vibration decomposition, and Wavelet Packet decomposition) have been used to estimate the known imbalance of car wheels. In the dynamic one, the weights are computed at runtime by estimating the difference between each predictor and the actual signal. The hybrid approach is the combination of the two algorithms. Results of simulations and experiments evidenced the validity of the data fusion, with uncertainty reductions between 10 and 40%, with larger benefits in presence of non-stationary disturbances.

$$\varphi \hbox {FrMF}$$ φ FrMF : Fractional Fourier Matched Filter

The paper develops a new interpretation of matched filtering of non-stationary signals in the fractional Fourier transform domain, which is beneficial for non-stationary/time–frequency signal processing applications. In the pursuit of establishing the theoretical and methodical foundation of matched filtering of non-stationary signal in the presence of non-stationary disturbance, the fractional-order signal processing technique is utilized. So in this context, it presents novel idea of improving the performance of non-stationary radar matched filtering in non-stationary disturbance as compared to classical approach. On the basis of derived analytical formulation, all theoretical results of proposed \(\varvec{\varphi }\)th fractional matched filter (\(\varvec{\varphi } \hbox {FrMF}\)) are corroborated by experimental results.

Identification techniques for stiction quantification in the presence of nonstationary disturbances

The paper presents a detailed comparison of different identification techniques applied to valve stiction quantification, possibly in the presence of nonstationary unknown disturbances. The control loop with sticky valve is modeled as a Hammerstein system, in which the nonlinearity is identified using enumeration of the parameters’ space. Five different techniques for identification of the linear model are compared in terms of achievable performance. In particular, the capability to cope with the presence of nonstationary disturbances is analyzed. The techniques allow one to estimate the unknown actual valve position (MV), without requiring any process knowledge, being based only on data which are usually recorded in industrial plants: controller output (OP) and controlled variable (PV). Simulations show that external perturbations can be tolerated, thus ensuring a reliable evaluation of stiction in practical situations where external disturbances are usually present. Models which incorporate a time varying additive nonstationary disturbance grant a better process identification and a more accurate stiction estimation in the case of disturbance acting simultaneously with valve stiction. However, simpler models are the best choice when stiction happens to be the only source of loop oscillation. Results are confirmed by application to real data: pilot plant data are used to corroborate the effectiveness of the techniques.

MULTIRATE MULTIVARIABLE SELF-TUNING CONTROL OF DISTILLATION COLUMNS USING KALMAN PREDICTOR

Abstract A novel method for designing Kalman Predictor (KP) based multivariable self-tuning controllers for Multi Input Multi Output (MIMO) systems has been proposed, and also applied to the control of two distillation columns. The objective is to maintain constant terminal compositions despite disturbances entering the system even when the controlled variables are not measured at the same sampling rate. The KP generates minimum variance estimates of the output variables. Simulation results show that KP based multirate multivariable self-tuning controller exhibits better performance than the earlier reported multirate controller for set point tracking, even in the presence of nonstationary disturbances. KP based self-tuning controller is, therefore, suitable for industrial applications.

Seigniorage and tax smoothing in the United States 1914–1986

When monetary and fiscal authorities cooperate to minimize the distortionary costs of financing an exogenous stream of government expenditures, Barro's tax-smoothing model implies a long-run relationship between tax revenues and inflation. Previous empirical tests of this relationship are reinterpreted in light of recent work on cointegration and are shown to hold only when stochastic temporal variation in the excess burden of taxes and seigniorage is transitory in nature. A new test that holds in the presence of nonstationary disturbances is developed and applied. Annual U.S. data from the period 1914 to 1986 reject the revenue-smoothing hypothesis.