Optimal Excitation Signal Research Articles

A non-invasive short range acoustic technique for liquid level measurement in containers

Techniques to measure liquid level in containers and vessels non-invasively are of great interest for the manufacturing, chemical industries, and energy sectors. Most commercial solutions use invasive techniques, which are not always practical and can lead to unsafe conditions when measuring devices encounter high pressures or hazardous chemical contents. In this study, a portable, non-invasive acoustic liquid level identification technique is developed. The technique relies on comprehensive analysis of acoustic guided wave modes in the container walls and their coupling to liquid contents to determine the optimal sensor separation distance and excitation signal characteristics, resulting in liquid level determination with high accuracy and precision. First, dispersion characteristics of guided wave propagation in container walls in contact with a liquid boundary are studied in detail. Based on the dispersion analysis a general-purpose short-range liquid level detection methodology is developed. The developed methodology is then validated using numerical wave propagation simulations and experiments on various metal containers with different physical characteristics, illustrating the versatility of the method. Compared to existing techniques, which are optimized for specific applications, the proposed method, due to its short range and non-invasive nature, has the potential for universal applicability on virtually any container, irrespective of its shape, size, wall-thickness, and whether it has any internal components.

Space-filling optimized excitation signals for nonlinear system identification of dynamic processes of a diesel engine

This paper examines the space-filling property and expected model quality of two recently developed excitation signals – the Global Optimized Amplitude Time Signal (GOATS) and the incremental Global Optimized Amplitude Time Signal (iGOATS) – on a real-world nonlinear dynamic system (the diesel engine) characterized by a high degree of system complexity. The diesel engine’s system complexity includes infeasible operating regions, strong nonlinearities, and multiple feedback paths. Additionally, a novel variant of the GOATS – the stacked Global Optimized Amplitude Time Signal (sGOATS) – is developed and investigated. For the analysis of the expected model quality and space-filling property of the GOATS, iGOATS, and sGOATS, they are compared to the state-of-the-art excitation signals Amplitude Pseudo Random Binary Signal (APRBS) and Optimized Nonlinear Input Signal (OMNIPUS) to approximate the exhaust pressure of a diesel engine depending on multiple inputs. The results show that the GOATS, iGOATS, and sGOATS outperform the APRBS and OMNIPUS regarding their space-filling property and expected model quality.

Feature fusion for pulse-modulation eddy current imaging and evaluation of defects in layered conductors

In this paper, the imaging and evaluation of defects hidden within the stratified conductor via pulse-modulation eddy current testing (PMEC) is intensively scrutinised through theoretical and experimental investigation. Following the proposition regarding the optimal excitation signal of PMEC, a fused signal feature integrating the harmonic and pulsed characteristics of PMEC signals is proposed in an effort to further enhance the testing sensitivity and signal-to-noise ratio (SNR). By comparing with the features of conventional PMEC and pulsed eddy current testing (PEC), the superiority of the fused feature is identified via theoretical simulations. This is subsequently supported by the corrosion imaging results obtained from the experiments, further indicating the advantage of the fused feature over traditional features in terms of testing sensitivity and SNR.

Low-power vibrothermography detection technique for surface cracks on composite sucker rod

Composite sucker rods are becoming increasingly popular in oil fields owing to their light weight and corrosion resistance. However, various defects may appear in the manufacturing and service of sucker rods. Unfortunately, research on defect detection is still insufficient for composite sucker rods. This study explores a surface crack detection technique for composite sucker rods based on low-power vibrothermography (LVT). First, an improved low-power piezoceramic actuator with a curved bottom is designed for rod-like structure. Second, the entire processes of LVT-based surface crack detection, including excitation signal generation, mechanical wave propagation, and heat generation at defects, are simulated and analyzed using the finite element method. The heat generation mechanism and characteristic at cracks are revealed by coupled thermo-electro-mechanical analysis. The superiority of the improved actuator is reflected by comparison with the conventional actuator. In addition, the simulation investigates the optimal excitation signal amplitude and circumferential angle between the actuator and the crack. Finally, a series of LVT detection experiments were conducted on a composite sucker rod with surface cracks, which not only proved the effectiveness of the LVT, but also validated the conclusions of the numerical simulations. The maximum temperature rise at the crack is 5.54 °C by using the improved actuator. Numerical simulation and experimental results demonstrate that the surface cracks of the composite sucker rod can be effectively detected using LVT with an improved piezoceramic actuator. This study provides a new direction for extending vibrothermography to the non-destructive testing of composite rod-like structures.

Horizontal beam response at extraction conditions at the Heidelberg Ion-beam Therapy Centre

The Heidelberg Ion-Beam Therapy Centre’s synchrotron makes use of the sextupole driven RF-KO method near the third-order resonance in order to slowly extract the beam that is delivered to the patients. The horizontal beam response of a coasting beam was studied experimentally and with simulations at extraction conditions in order to deduce regions of interest for an optimal excitation signal spectrum. Two narrow frequency regions were found where the beam reacts coherently. With these information an RF signal was proposed for the resonant slow extraction.

Selective simultaneous generation of distinct unidirectional wave modes in different directions using dual-array transducer

Shear horizontal (SH) waves are widely used in the non-destructive evaluation and structural health monitoring fields. Conventional periodic permanent magnet electromagnetic acoustic transducers (PPM EMAT) can generate a single SH wave mode, which propagates in both forward and backward directions. Generation in a single direction is usually preferred to ease signal interpretation and increase inspection reliability. Recently, we have proved that a single, either fundamental or high-order, SH wave mode can be unidirectionally generated using a dual PPM EMAT, with carefully designed driving signals. Distinct SH modes present different applicability due to their particular response to features in the structure, such as defects. Hence, unidirectional generation of two different SH modes simultaneously with a dual-array transducer is investigated in this paper. The selectivity of wave modes and the selectivity of the propagation direction are studied by combining pure wave mode excitability with the superposition of optimal excitation signals for unidirectional generation, addressed in the frequency–wavenumber domain. Excitation signals for optimal and simultaneous generation of the SH0 and SH1 modes propagating in the same direction or in opposite directions have been designed. Experiments on an aluminium plate have demonstrated that the two wave modes can be effectively simultaneously generated with only one dual-array transducer and the propagation direction of each wave mode can be flexibly controlled using the designed excitation signals.

Optimization of the spill quality for the hadron therapy at the Heidelberg Ion-Beam Therapy Centre

The Heidelberg Ion-Beam Therapy Centre’s synchrotron makes use of the well established slow extraction RF KO method near the third-order resonance. The spill quality is essential for therapy, since the reproducibility and fidelity in the parameters of the extracted beam translate in a reduction of irradiation time. The horizontal beam response was studied experimentally and with simulations at extraction conditions in order to deduce regions of interest for an optimal excitation signal spectrum. Two narrow frequency regions were found near the betatron resonance. With these results a new signal was engineered for the emittance blow-up scheme. The new excitation signal improves significantly the spill quality for the extraction configurations; for instance the lower interquartile value of the spill duty factor distribution for the most rigid carbon-ion beam Ekin= 430MeV/u improves from 82.2% to 94.8%. For the proton beam the improvement is more significant, where the spill duty factor is kept over the 90% level.

Optimal unidirectional generation of a dispersive wave mode with dual-array transducer

Array transducers are commonly used to generate several types of waves, such as ultrasonic guided waves. They consist of a series of periodic wave source elements, and thus define a nominal or dominant wavelength of generated waves. A common approach to generating waves in a single direction is to use two identical wave sources, or two arrays of sources forming a dual-array transducer, that are separated by a quarter wavelength. Then, one source is activated a quarter of a time period after the other. In this way, constructive interference arises in one direction, whilst destructive interference arises in the opposite direction, making the total wave source unidirectional. This principle assumes that the frequency content of the generated wave is monochromatic. When there is some frequency bandwidth to the wave generated, such as with finite-length pulses, one cannot satisfy simultaneously constructive and destructive interference in opposite directions with dual-array transducers. For a non-dispersive wave mode, ideal destructive interference for pulsed waveform is obtained if one of the sources is inverted relative to the other, so that, in theory, complete cancellation can be achieved. Moreover, most guided wave modes are dispersive, meaning that, different frequencies propagate at different phase speeds. This limits the amount of wave cancellation in one direction and constructive interference in the other. Here, we describe the operation of the dual-array transducer in the frequency–wavenumber domain and use it to propose two new excitation methods, based on a frequency-dependent phase-shift strategy, that is designed through the dispersion relationship of the wave mode of interest. These provide ideal constructive or destructive interferences for dispersive waves, and achieve optimal unidirectional generation behaviour for dispersive wave modes. The new methods were experimentally assessed with shear horizontal ultrasonic waves, generated by a dual periodic permanent magnet electromagnet acoustic transducer. The optimal excitation signal yielded up to 30 dB unidirectionality, when generating the dispersive SH1 wave mode in an aluminium plate.

Quasi-Load Insensitive Doherty PA Using Supply Voltage and Input Excitation Adaptation

This article presents a quasi-load insensitive (QLI) Doherty power amplifier (DPA). The proposed theory makes the amplifier load insensitive in terms of output power, while its efficiency is slightly degraded for complex loads. The load insensitiveness is achieved by dynamically changing the supply voltages ( <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>${V_{DD}}$</tex> </formula> ) and the input power splitting for both the carrier and peaking transistors. The optimal, load-dependent, <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>${V_{DD}}$</tex> </formula> values are theoretically derived from back-off (BO) and full power conditions using load line theory. The optimal input excitation signals for the carrier and peaking devices are also derived for these variable <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex>${V_{DD}}$</tex> </formula> conditions. A 3.6-GHz QLI DPA was designed, and a complete system, composed of the DPA output stage, a two-channel medium power driver, an adaptive input driving stage, and a load sensing system, was implemented. The laboratory measurements have been performed for loads distributed inside a 2.0 maximum voltage standing wave ratio (VSWR) circle and show an output power variation between 43.8 and 42.6 dBm and a BO efficiency between 50% and 35%. Under modulated signal excitation, for the worst case loads, the peak output power capability of the DPA is improved from 41.7 to 43.1 dBm, and the average efficiency is increased from 32.6% to 43%.

Development of Optimal Parameter Estimation Methodologies Applied to a 3DOF Autonomous Surface Vessel

Autonomous Surface Vessels (ASVs) are reliable and robust vehicles. They perform autonomous missions in lakes, rivers and even open waters. Those are dangerous environments that requires precise and secure navigation. Under these conditions, the knowledge of a robust and accurate mathematical model is a fundamental aspect for adjusting the control system for reaching safety and performance. Moreover, traditional mathematical models disregard assimetries and coupling between the degrees of freedom. While those models work fine for bigger vessels, to ignore these characteristics in small ASVs compromises the model's quality. In this context, this work presents a new methodology for modeling and identifying the dynamics of ASVs along with the uncertainties arising from disturbances and non-modeled dynamics. As for the uncertainties and disturbances, this work considers the coupling parameters into the mathematical modeling, which synthesizes the divergences between the model and the real application, allowing to incorporate asymmetries and model deficiencies. Regarding the parameter identification, the proposal is based on (i) the design of optimal input excitation signals from a double layer optimization methodology and (ii) a parametric estimation concept in two steps, dividing the original set of parameters into two partially coupled sub-problems. Finally, this work also presents a full discussion and analysis about the importance to manage the trade-off between precision and complexity of mathematical models, its respective solution spaces and the impact over the optimization algorithms. To validate the approach, a real 3 Degree of Freedom ASV with aerial holonomic propulsion system is used. The results show that it is possible to successfully capture the complex set of parameters and identify physical characteristics not considered by the model.

Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design.

As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F1 and F3, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.

A Probabilistic Approach to Robust Optimal Experiment Design with Chance Constraints

Accurate estimation of parameters is paramount in developing high-fidelity models for complex dynamical systems. Model-based optimal experiment design (OED) approaches enable systematic design of experiments to generate input-output data sets with high information content for parameter estimation. Standard OED approaches however face two challenges: (i) experiment design under incomplete system information due to unknown true parameters, which usually requires many iterations of OED; and (ii) incapability of systematically accounting for the inherent uncertainties of complex systems, which can lead to diminished effectiveness of the designed optimal excitation signal as well as violation of system constraints. This paper presents a robust OED approach for nonlinear systems with arbitrarily-shaped time-invariant probabilistic uncertainties. Polynomial chaos is used for efficient uncertainty propagation. The distinct feature of the robust OED approach is the inclusion of chance constraints to ensure constraint satisfaction in a stochastic setting. The presented approach is demonstrated by optimal experimental design for the JAK-STAT5 signaling pathway, which regulates various cellular processes in a biological cell.

Info-gap robustness of an input signal optimization algorithm for damage detection

Info-Gap Decision Theory is adopted to assess the robustness of a technique aimed at identifying the optimal excitation signal to be used for active sensing approaches to damage detection. Here the term “active sensing” refers to procedures where a known input is applied to the structure to enhance the damage detection process. Given limited system response measurements and ever-present physical limits on the level of excitation, the ultimate goal of the mentioned technique is to improve the detectability of damage by increasing the difference between measured outputs of the undamaged and damaged systems. In particular, a two degree-of-freedom mass–spring–damper system characterized by the presence of a nonlinear stiffness is considered. Uncertainty is introduced to the system in the form of deviations of its parameters (mass, stiffness, damping ratio) from their nominal values. Variations in the performance of the mentioned technique are then evaluated both in terms of changes in the estimated difference between the responses of the damaged and undamaged systems and in terms of deviations of the identified optimal input signal from its nominal estimation. Finally, plots of the performances of the analyzed algorithm for different levels of uncertainty are obtained, enabling a clear evaluation of the risks connected with designing excitation signals for damage detection, when the parameters that dictate system behavior (e.g. stiffness, mass) are poorly characterized or improperly modeled.

Multisine excitation design to increase the efficiency of system identification analysis through undersampling and DFT optimization

Multisine excitations are commonly employed to measure the frequency response of a nonlinear system. A typical pseudo-logarithmically-spaced frequency distribution may be oversampled at a rate significantly greater than the Nyquist rate to ensure accurate signal reconstruction without aliasing. In this paper, two algorithms are presented for optimizing the frequency distribution of a multisine excitation signal such that the system output can be undersampled without corruption, resulting in compact DFT bin utilization. In addition, the optimized excitation signal is designed to approximate a user-defined frequency distribution and make allowances for harmonic frequencies generated by system nonlinearities. Results show that at least an eleven-fold improvement in DFT bin utilization is possible for an example two-decade logarithmically-spaced 25-tone excitation signal applied to a nonlinear system exhibiting both second and third order harmonics. The use of optimized excitation signals in power constrained applications, such as structural health monitoring, can help to increase adoption rates by reducing system complexity and power source requirements.

On assessing the robustness of an input signal optimization algorithm for damage detection: the Info-Gap Decision Theory approach

The Info-Gap Decision Theory (IGDT) is here adopted to assess the robust- ness of a technique aimed at identifying the optimal excitation signal within a structural health monitoring (SHM) procedure. Given limited system response measurements and ever-present physical limits on the level of excitation, the ultimate goal of the mentioned technique is to improve the detectability of the damage increasing the difference between measurable outputs of the undamaged and damaged system. In particular, a 2 DOF mass- spring-damper system characterized by the presence of a nonlinear stiffness is considered. Uncertainty is introduced within the system under the form of deviations of its parameters (mass, stiffness, damping ratio...) from their nominal values. Variations in the performance of the mentioned technique are then evaluated both in terms of changes in the estimated difference between the responses of the damaged and undamaged system and in terms of deviations of the identified optimal input signal from its nominal estimation. Finally, plots of the performances of the analyzed algorithm for different levels of uncertainty are ob- tained, showing which parameters are more sensitive to the presence of uncertainty and thus enabling a clear evaluation of its robustness.

Optimal Excitation Signal Design for Identification of a Coarse-fine Motion Platform

Identification excitation signal design method is improved for a coarse-fine motion platform. A coarse-fine platform structure is widely used in long stroke, high precision motion platform systems, such as scan lithography tool motion platform and hard disk track-seeking platform. Obtaining an accurate model of course-fine system is an important basis for improving the control of the system, while system identification is an effective way to obtain the system model, in which the design of input signal is an important part of the test of system identification. The system model of coarse-fine motion platform is simplified to a multivariable finite dimensional linear time invariant discrete-time model. Multiple-input multiple-output (MIMO) identification method is introduced into coarse-fine system identification. An optimization design algorithm of excitation signal of multiple-input multiple-output model system identification is proposed. The algorithm parameterizes the multi-sine curve time domain signal and its power spectrum density. Then the parameterized excitation signal is optimized under limited power output. The results of theoretical analysis and simulation show that this method can effectively improve the performance of asymptotic covariance of the estimation model.

Iterative identification and control design with optimal excitation signals based on υ-gap

An iterative identification and control design method based on υ-gap is given to ensure the stability of closed-loop system and control performance improvement. The whole iterative procedure includes three parts: the optimal excitation signals design, the uncertainty model set identification and the stable controller design. Firstly the worst case υ-gap is used as the criterion of the optimal excitation signals design, and the design is performed via the power spectrum optimization. And then, an uncertainty model set is attained by system identification on the basis of the measure signals. The controller is designed to ensure the stability of closed-loop system and the closed-loop performance improvement. Simulation result shows that the proposed method has good convergence and closed-loop control performance.

Optimized Excitation Signals for MIMO Frequency Response Function Measurements

Many different excitation signals can be chosen for the nonparametric frequency response function measurements of a linear multiple-input-multiple-output (MIMO) system. In the presence of output noise they will result in different signal-to-noise ratios (SNRs) of the frequency response function measurements. In this paper, these effects are analyzed and compared for three classes of multisine excitations: random multisines, approximately orthogonal random multisines, and orthogonal random multisines, taking also into account the crest-factor minimization. It is shown that the orthogonalization of the inputs yields an essential increase in the measured SNR

Identification of delay dominant recycle systems

A new identification method for single-input–single-output delay dominant recycle systems is presented in this paper. Identification of recycle systems is similar to that of closed loop systems. However, identification of recycle systems poses certain challenges in that the input-equivalent signal in the closed loop identification is not available for recycle systems. Therefore, special identification routines are required to ensure consistency of recycle and forward models. It is shown that consistent estimates of delay dominant recycle systems can be obtained by treating a delayed output as one of the inputs. Asymptotic variance expressions for the estimates of forward and recycle models are provided. These are then used in designing an optimal excitation signal for recycle systems. The results are illustrated through an industrial example.

The Cramer-Rao Bound and DMT Signal Optimisation for the Identification of a Wiener-Type Model

In linear system identification, optimal excitation signals can be determined using the Cramer-Rao bound. This problem has not been thoroughly studied for the nonlinear case. In this work, the Cramer-Rao bound for a factorisable Volterra model is derived. The analytical result is supported with simulation examples. The bound is then used to find the optimal excitation signal out of the class of discrete multitone signals. As the model is nonlinear in the parameters, the bound depends on the model parameters themselves. On this basis, a three-step identification procedure is proposed. To illustrate the procedure, signal optimisation is explicitly performed for a third-order nonlinear model. Methods of nonlinear optimisation are applied for the parameter estimation of the model. As a baseline, the problem of optimal discrete multitone signals for linear FIR filter estimation is reviewed.