Modal regulator of drive moving electrode of the arc furnace

В роботі розглянуто питання керування дугової сталеплавильної печі. Розрахунки проводяться на основі лінеаризованої математичної моделі системи печі. Складено матричну форму представленої моделі в змінних стану. Порівняння результатів матричних обчислень з біноміальним розподілом Ньютона дозволило розробити структуру керуючого модального регулятора. Проведено порівняне моделювання дій систем дугової сталеплавильної печі, що керуються класичним та модальним регуляторами. Порівняння показало, що використання розробленого модального регулятора дозволяє мінімізувати величину перерегулювання та тривалість перехідного процесу при випадкових зрушеннях режиму плавки.In this article the questions of management of the arc-furnace (EAF) are considered. The aim is to construction of automatic furnace regulator. The basis for constructing such a regulator is the mathematical model of the control object – EAF. Experience shows that the use of classical automatic regulators does not provide sufficient quality control of the EAF modes. It is proposed to use a modal regulator. The procedure for calculating the parameters of a modal regulator for a particleboard system is given. The basis for the calculation is a linearized mathematical model of the EAF system. Basic calculations are performed using standard software. Based on the linearized model of the chipboard, a matrix form of the model is constructed in the state of the variables. Using the matrix calculations we obtain the characteristic equation of a closed system of EAF. Comparing the obtained expression with the standard binomial distribution of Newton we calculate the coefficients of the modal regulator for EAF. In steady state, the current of the arc should be equal to the given value, and all increments of variables should be equal to zero. From these conditions we calculate the last coefficient of the modal controller from the system of equations, which is represented in the matrix form. The proposed procedure for calculating the modal regulator for EAF system is relatively simple. It does not require significant computational resources, even in the case of such a complex control object as the EAF. A comparative modeling of the control system of the EAF with the synthesized modal and classical regulators was carried out. The simulation results indicate a shorter duration of transients and a low level of overregulation of the drive for moving the electrodes of the chipboard. The use of the developed modal regulator should provide an opportunity for better control of the main processes of chipboard. In addition, such a control system avoids the share of emergency situations that occur when the charge is melting

Innovative system identification methods for monitoring applications

Monitoring the modal parameters of civil and mechanical system received plenty of interest the last decades. Several approaches have been proposed and successfully applied in civil engineering for structural health monitoring of bridges (mainly based on the monitoring of the resonant frequencies and mode shapes). In applications such as the monitoring of offshore wind turbines and flight flutter testing the monitoring of the damping ratios are essential. For offshore wind turbine monitoring the presence of time-varying harmonic components, close to the modes of interest, can complicate the identification process. The difficulty related to flight flutter testing is that, in general, only short data records are available. The aim of this contribution is to introduce system identification methods and monitoring strategies that result in more reliable decisions and that can cope with complex monitoring applications. Basic concepts of system identification will be recapitulated with attention for monitoring aspects. The proposed monitoring methodology is based on the recently introduced Transmissibility-based Operational Modal Analysis (TOMA) approach

Kalman filter-based subspace identification for operational modal analysis under unmeasured periodic excitation

International audienceThe modes of linear time invariant mechanical systems can be estimated from output-only vibration measurements under ambient excitation conditions with subspace-based system identification methods. In the presence of additional unmeasured periodic excitation, for example due to rotating machinery, the measurements can be described by a state-space model where the periodic input dynamics appear as a subsystem in addition to the structural system of interest. While subspace identification is still consistent in this case, the periodic input may render the modal parameter estimation difficult, and periodic modes often disturb the estimation of close structural modes. The aim of this work is to develop a subspace identification method for the estimation of the structural parameters while rejecting the influence of the periodic input. In the proposed approach, the periodic information is estimated from the data with a non-steady state Kalman filter, and then removed from the original output signal by an orthogonal projection. Consequently, the parameters of the periodic subsystem are rejected from the estimates, and it is shown that the modes of the structural system are consistently estimated. Furthermore, standard data analysis procedures, like the stabilization diagram, are easier to interpret. The proposed method is validated on Monte Carlo simulations and applied to both a laboratory example and a full-scale structure in operation

진동 특성 개선을 위한 개발 과정의 새로운 접근법 연구: 결합부 특성 파악 및 상대 민감도 분석

학위논문 (박사)-- 서울대학교 대학원 공과대학 기계항공공학부, 2017. 8. 강연준.When analyzing the components assembled compactly in a system for setting the shaker or measuring an impact force exerted on the component correctly, the measurement errors caused by an incorrectly measured force could be increased. Transmissibility includes only response data, unlike FRFs that include force measurements. In this thesis, new approaches for developing process of vibrational characteristic improvement that consider boundary properteis and sensitivity of responses are presented. Transmissibility concepts is adopted to identify the boundary properties and to suggest indices for relative sensitivity analysis. A new method for identifying boundary properties are proposed. Equation for estimating boundary properties is derived by investigating the difference in transmissibilities between a component under the coupled condition and under the free condition. Discrete multiple degrees of freedom system with single boundary and multiple boundary conditions are used to verify of the method. The method is also applied to a beam which is the simplest structural form of continuous system to investigate whether the method still usable in practical condition. Good agreement is achieved when estimated properties are compared with exact properties. Further, Error equation using measurement noise is developed to assess the robustness of the method for application under practical conditions. In addition, indices based on the transmissibility are suggested to analyze relative sensitivity of responses. Relative senstivity of responses with respect to variables should be analyzed to make small design modifications for improving the vibrational charactertistics of a system. Two types of indices with respect to variables are developed for indicating an appropriate position where the design variable could be modified and indicating an effect of the specific design variable on the responses. Discrete multiple degrees of freedom system and two numerical beam models are used to investigate whether the proposed indices reflect the relative changes in response to small design modifications. It has been found that the proposed indices exactly represent the sensitivity characteristics of the system by showing that the indices agreed well with the indicators for all frequency ranges.ABSTRACT --------------------------------------------------------------------------ⅰ TABLE OF CONTENTS ---------------------------------------------------------- iv LIST OF TABLES ----------------------------------------------------------------- vii LIST OF FIGURES --------------------------------------------------------------- viii CHAPTER 1. INTRODUCTION ------------------------------------------------- 1 CHAPTER 2. CONCEPT OF TRANSMISSIBILITY ---------------------- 10 2.1 Introduction --------------------------------------------------------------- 10 2.2 Formulation of transmissibility ----------------------------------------- 11 CHAPTER 3. IDENTIFICATION OF BOUNDARY CHARACTERISTICS ----------------------------------------------------- 16 3.1 Introduction --------------------------------------------------------------- 16 3.2 FRF estimation using boundary properties --------------------------- 18 3.3 Theoretical formulation for estimation of boundary characteristic matrix -------------------------------------------------------------------------- 20 3.4 Verification and application examples --------------------------------- 26 3.4.1 Verification: 2-DOF discrete system with single boundary condition ----------------------------------------------------------------- 26 3.4.2 Verification: 4-DOF discrete system with multiple boundary conditions ---------------------------------------------------------------- 30 3.4.3 Finite beam model with multiple boundary conditions ----- 34 3.4.4 Effects of the numbers and positions of DOFs --------------- 46 3.5 Error analysis for assessment of robustness -------------------------- 52 3.5.1 Derivation of error equation ------------------------------------ 52 3.5.2 Effects of the measurement noise ------------------------------ 56 3.5.3 Comparison of estimated properties with and without measurement noise ----------------------------------------------------- 59 3.5.4 Comparison of estimation errors with and without measurement noise ----------------------------------------------------- 63 3.6 Summary and Conclusion ----------------------------------------------- 66 CHAPTER 4. SENSITIVITY INDICES FOR RELATIVE SENSITIVITY ANALYSIS ------------------------------------------------------------------- 68 4.1 Introduction --------------------------------------------------------------- 68 4.2 Sensitivity index ---------------------------------------------------------- 70 4.2.1 Sensitivity indices related to mass ----------------------------- 74 4.2.1.1 Sensitivity index for positions of variable: SI(mi, rk) --------------------------------------------------------------------- 74 4.2.1.2 Sensitivity index for positions of response: SI(mk, ri) --------------------------------------------------------------------- 75 4.2.2 Sensitivity indices related to stiffness ------------------------- 76 4.2.2.1 Sensitivity index for positions of variable: SI(kij, ri) ---------------------------------------------------------------------- 76 4.2.2.2 Sensitivity index for positions of response: SI(kkl, ri) --------------------------------------------------------------------- 77 4.2.3 Sensitivity indices related to damping ------------------------- 79 4.2.3.1 Sensitivity index for positions of variable: SI(cij, ri) ---------------------------------------------------------------------- 79 4.2.3.2 Sensitivity index for positions of response: SI(ckl, ri) --------------------------------------------------------------------- 79 4.3 Verification and application examples --------------------------------- 80 4.3.1 Verification: MDOF discrete model --------------------------- 80 4.3.2 Finite beam model ----------------------------------------------- 88 4.3.2.1 Results for the mass variable ------------------------- 88 4.3.2.2 Results for the stiffness variable --------------------- 95 4.4 Summary and conclusion ---------------------------------------------- 103 CHAPTER 5. Conclusions ------------------------------------------------------- 104 REFERENCES -------------------------------------------------------------------- 108 국 문 초 록------------------------------------------------------------------------ 117Docto

Operational modal analysis and prediction of remaining useful life for rotating machinery

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe significance of rotating machinery spans areas from household items to vital industry sectors, such as aerospace, automotive, railway, sea transport, resource extraction, and manufacturing. Hence, our technologised society depends on efficient and reliable operation of rotating machinery. To contribute to this aim, this thesis leverages measurable quantities during its operation for structural-mechanical evaluation employing Operational Modal Analysis (OMA) and the prediction of Remaining Useful Life (RUL). Modal parameters determined by OMA are central for the design, test, and validation of rotating machinery. This thesis introduces the first open parametric simulation dataset of rotating machinery during an acceleration run. As there is a lack of similar open datasets suitable for OMA, it lays a foundation for improved reproducibility and comparability of future research. Based on this, the Averaged Order-Based Modal Analysis (AOBMA) method is developed. The novel addition of scaling and weighted averaging of individual machine orders in AOBMA alleviates the analysis effort of the existing Order-Based Modal Analysis (OBMA) method by providing a unified set of modal parameters with higher accuracy. As such, AOBMA showed a lower mean absolute relative error of 0.03 for damping ratio estimations across compared modes while OBMA provided an error value of 0.32 depending on the processed order. At excitation with high harmonic contributions, AOBMA also resulted in the highest number of accurately identified modes among the compared methods. At a harmonic ratio of 0.8, for example, AOBMA identified an average of 11.9 modes per estimation, while OBMA and baseline OMA followed with 9.5 and 9 modes, respectively. Moreover, it is the first study, which systematically evaluates the impact of excitation conditions on the compared methods and finds an advantage of OBMA and AOBMA over traditional OMA regarding mode shape estimation accuracy. While OMA can be used to evaluate significant structural changes, Machine Learning (ML) methods have seen substantially greater success in condition monitoring, including RUL prediction. However, as these methods often require large amounts of time and cost- intensive training data, a novel data-efficient RUL prediction methodology is introduced, taking advantage of distinct healthy and faulty condition data. When the number of training sequences from an open dataset is reduced to 5%, an average prediction Root Mean Square Error (RMSE) of 24.9 operation cycles is achieved, outperforming the baseline method with an RMSE of 28.1. Motivated by environmental considerations, the impact of data reduction on the training duration of several method variants is quantified. When the full training set is utilised, the most resource-saving variant of the proposed approach achieves an average training duration of 8.9% compared to the baseline method

New Methods for Structural Health Monitoring and Damage Localization

Structural Health Monitoring (SHM) is traditionally concerned with fitting sensors inside structural systems and analyzing the features of signals from the sensor measurements using appropriate signal processing techniques in order to reveal the systems’ health status. A significant change of signal features is often considered to be an indication of damage. However, generally speaking, these techniques often cannot distinguish normal structural changes due to variations in system environmental or operating conditions from the changes which are induced by damage. For example, transmissibility analysis is a widely used signal analysis method for SHM. But traditional transmissibility is determined by the ratio of the spectra of two different system outputs, which generally depends on the location of loadings on the system and is, consequently, affected by system environmental conditions. In order to solve this challenge, a series of studies are conducted in this PhD project. The objectives are to develop new SHM and damage localization methods, which can effectively address the effects of changing system environmental or operational conditions and have potential to be applied in practice to more effectively solve practical SHM and damage localization problems. First, a general baseline model based SHM method is developed in this thesis. This method can be used to address a wide class of SHM problems via a baseline modelling and baseline model based analysis. The method can systematically take the effects of system’s operating or environmental conditions such as, e.g., environmental temperature etc. on signal analysis into account, and can therefore solve relevant challenges. Both simulation studies and field data analyses have been conducted to demonstrate the performance of the proposed new technique. Moreover, new transmissibility analysis methods are proposed for the detection and location of damage with nonlinear features in Multi-Degree-Of-Freedom (MDOF) structural systems. These methods extend the traditional transmissibility analysis to the nonlinear case. More importantly, the methods are independent from the locations of loading inputs to the systems and, to a great extent, provide effective solutions to the above mentioned problems with traditional transmissibility analysis. Again both numerical simulation studies and experimental data analysis have been conducted to verify the effectiveness and demonstrate potential practical applications of the new methods. Based on the results of nonlinearity detection and localization, new guidelines are proposed for the application of transmissibility analysis based modal identification method to nonlinear structural systems, which have potential to be further developed into a new approach to transmissibility based nonlinear modal analysis. In summary, the present study has addressed a series of fundamental problems with SHM, especially, problems associated with how to deal with the effects of changing system environmental or operational conditions on SHM results. Experimental studies have demonstrated the potential and significance of these results in practical engineering applications