System Output Response Research Articles

Guided wave localization of small defects based on stochastic resonance characteristics of Duffing systems

To improve the location accuracy of small defects in anti-corrosion pipelines by using ultrasonic guided wave, this study developed a guided wave location method for small defects based on stochastic resonance (SR) characteristics of a time-moving Duffing system. Firstly, it solved the shortcomings of the selection of the key parameters of the system, which depended on human subjectivity and lacked interaction correlation in the previous research. By using the stochastic resonance characteristics of the Duffing system, the best fit parameter group of the system was determined synchronously and coordinately. Secondly, with the conjoint analysis of the key parameters of the system and the numerical simulation verification method, found that part of the energy of the noise signal would be transferred to the system output response, leading to the system output response was strengthened. Finally, A time-moving window function was introduced in the experimental verification, and the enhanced system output response in the window was analyzed using power spectral density (PSD), which could realize the location of guided waves with small defects. The experimental result showed that the small defect guided wave with a cross-section loss rate of at least 6 % could be effectively located.

Global sensitivity analysis and optimization of multi-stage TEG based on random and fuzzy mixed input variables

In practical engineering, uncertainties in thermoelectric power generation arise from various factors such as material properties, geometric dimensions, assembly processes, and operational fluctuations of the thermoelectric components. Addressing subjective and objective uncertainties in the design parameters of multi-stage thermoelectric generators (TEGs), this study establishes a robust TEG model incorporating non-equilibrium thermodynamics. Fuzzy and stochastic theories are employed for uncertainty representation, and a variance-based global sensitivity analysis is conducted. The results indicate that the uncertainties in the design variables have varying effects on the output responses of the three-stage TEG. Among them, the temperature of the hot and cold sources has the most significant impact on the output responses（STHP: 0.5018, STLP:0.2199, STHη: 0.5436, STLη: 0.3336）, Parameters like Seebeck coefficient, thermal resistance, thermal conductivity, and operating current exhibit relatively minor effects. The coefficient of variation of the output response varies consistently with changes in the standard deviation or coefficient of variation of input variables, following a similar order as the sensitivity indicators. The variations in the distribution parameters of the key design variable, i.e., the temperature of the hot and cold sources, do not affect the sensitivity index rankings of the variables concerning the system output responses. As the distribution parameters of the hot and cold source temperatures gradually increase, the sensitivity indices of the design variables to the system output responses exhibit different trends, indicating a change in the degree of sensitivity to the variations in distribution parameters of the design variables. Optimization based on global sensitivity analysis enhances power output by 43.37 %, reaching a maximum of 11.37 W under constraints compared to the original 7.93 W. This research provides guidance for designing and optimizing thermoelectric power systems under mixed uncertainties.

Optimal power control in Renewable Energy sources using Intelligence algorithm

An intelligent, Adaptive Neuro-Fuzzy Inference System (ANFIS) based Xilinx Power Management system is proposed for the Hybrid renewable energy sources (HRES). The Xilinx Power Management generator block set (ANFIS-XL-PMS) controller is implemented for the purpose to control and avoid the non-linear nature opted in the HRES power system. Solar (PV), Wind Energy (WE), and Fuel cell (FC) are the primary power sources of the system, and a battery storage system (BES) is used as a backup. A comprehensive power management strategy is intended for the proposed system to control the flow of power between the various energy sources and the system's storage device. The simulation model for the HRES is elegantly designed using the MATLAB/Simulink platform and the Switching patterns are generated using the XLPMS controller. The system performance and output responses of the SMC-XL-ANFIS controller have been verified by carrying out the simulation studies and compared with the existing controller like Sliding Mode Controller (SMC-XL-PMS), and Artificial Neutral Network (ANN-XL-PMS). Simulation results show good performance in terms of voltage and current transient, settling time, load power efficiency, and power quality Also a Prototype model with a PIC microcontroller is designed and the output responses are analyzed.

Solar-powered five-leg inverter-driven quasi-dynamic charging for a slow-moving vehicle

Road transport is becoming increasingly electric as it becomes more environmentally friendly. A green transportation system includes solar arrays along the roadside, encouraging the eco-friendly EV charging system. This paper proposes a road-side photovoltaic system to charge the batteries of slow-moving electric vehicles using a five-leg inverter. The five-legged inverter, which utilizes a stand-alone PV system to drive the charging pads, enhances the probability of achieving the sustainability goal. The limitations of the conventional H-bridge inverter, such as its more prominent design and higher number of switches or straightforward design and restricted power level, are addressed by this converter. The proposed 3.3 kW, 85 kHz inverter energizes the four transmitter pads while a receiver pad moves over the transmitter pads and inductively extracts the power. The d.c.-d.c. converter is used to feed the power to the proposed inverter. The P and O-based MPPT algorithm with a tuned PI controller is used to generate the driving pulses of the d.c.-d.c. converter. The signals are generated based on the voltage and current output of the solar panel output. This control algorithm ensures the stability of the system output response. Additionally, the tuned d.c.-d.c. converter achieves maximum efficiency independent of the load resistance. The system maintains constant power transfer profile concerning load resistance variations. The 520*520 mm Double D-pad transmits the power, while the series-series compensation network assists the charging pads in achieving resonance. The developed systems’ nominal charging voltage and current are 144 V, 20 A, with an equivalent battery resistance of 7.2 Ω.

PI Control of Loudspeakers Based on Linear Fractional Order Model

This paper aims at the proportional-integral (PI) control of the cone vibration of the electrodynamic loudspeakers system recently described using a linear fractional order model. After introducing the fractional order model of the circuit of these loudspeakers, firstly, a new method is developed to design a fractional order PI controller to place the poles of the system in a desired area of the complex plane which is called D-stabilizing. The design parameters of the method depend directly on the speed of the system output response, the cone vibration. Moreover, the offered fractional order controller avoids any non-minimum phase zero, which causes undesired undershoots in the output, for the closed-loop control system. Secondly, considering uncertainties in the coefficients of the model, a methodology is presented to determine up to how much the uncertainties can increase such that the controller is still able to maintain both D-stability and the absence of non-minimum phase zeros for the control system. Finally, the merit of the presented results and the superiority of the designed fractional order controller over its conventional integer order counterpart are illustrated through numerical simulations.

Special issue on PID control in the information age: Theoretical advances and applications

Special issue on PID control in the information age: Theoretical advances and applications

An adaptive fractional stochastic resonance method based on weighted correctional signal-to-noise ratio and its application in fault feature enhancement of wind turbine

Stochastic resonance (SR) is an effective tool to enhance weak signal by utilizing noise to reach a certain synergistic effect, which has been widely studied in the field of weak signal detection. Currently, using SR to enhance the weak fault feature of wind turbine faces two challenges: First, it is difficult for SR to select the optimal system parameters, while the traditional adaptive method based on SNR needs to predict the precise frequency of the target signal. Second, the wind turbine load changes frequently, making the vibration and noise large. As a result, the traditional SR cannot effectively highlight the target fault feature by inducing a stable resonance phenomenon at the target frequency. To improve the ability of SR to enhance the weak fault feature of wind turbine under strong noise, this paper proposes an adaptive fractional SR method based on weighted correctional signal-to-noise ratio (WCSNR). Firstly, the proposed method considers the adiabatic approximation applicable condition in the SR system and combines characteristics of the expected output signal to construct the WCSNR evaluation index to quantify the system output response, so that the system can adaptively obtain optimal parameters without predicting the accurate frequency of the target signal. Then, the fractional-order theory is applied to the SR system to overcome the shortcoming that the integer-order SR cannot induce stable resonance phenomenon at the target frequency when enhancing the fault feature of wind turbine, and use WCSNR to search for the optimal fractional order to further enhance the weak fault characteristics. Simulation and engineering actual data analysis results verify the effectiveness and superiority of the proposed method in the fault feature enhancement of wind turbine. The analysis results show that compared with the traditional SR method, the method proposed in this paper can more effectively reduce the interference of background noise and accurately enhance the weak fault feature.

RETRACTED: Monitoring Air Pada Water Torn Berbasis Android dan Mikrokontroller

Kendali dan monitoring air dalam sebuah tangki merupakan satu intrumentasi komputer yang diperlukan di industri. Tugas Akhir ini membahas tentang aplikasi monitoring air pada water torn secara otomatis berbasis android dan didukung oleh mikrokontroller. Salah satu bidang teknologi yang berkembang ialah teknologi mikrokontroller, dalam perancangan alat ini, menggunakan mikrokontroller NodeMCU sebagai pengolah data dan sensor ultrasonik sebagai input data serta buzzer, dan servo. Sensor ultrasonik merupakan suatu perangkat yang dapat mengukur jarak suatu objek dengan memanfaatkan pantulan gelombang ultrasonik. Monitoring air ini menggunakan firebase sebagai database untuk menyimpan data. Untuk menampilkan data pengukuran, perangkat antar muka dirancang pula dengan menggunakan perangkat lunak aplikasi android studio dalam bentuk grafik, status pompa, dan notifikasi air. Hasil pengujian menunjukan bahwa sensor ultrasonik yang digunakan mampu mengukur ketinggian air dari 2 cm sampai 20 cm dengan rata-rata kesalahan pengukuran 4,93%. Monitoring air yang dirancang mampu menghasilkan respon keluaran sistem sesuai dengan nilai referensi yang diberikan. Sebagai tambahan, monitoring ketinggian muka air dapat ditampilkan pada perangkat lunak android secara interaktif

Aplikasi Monitoring Air Pada Water Torn Secara Otomatis Berbasis Android dan Di Dukung Oleh Mikrokontroller

Control and monitoring of water in a tank is a computer instrumentation needed in industry. This research discusses a water monitoring application in water torn automatically based on android and supported by a microcontroller. One of the developing technology fields is microcontroller technology. In designing this device, it uses a NodeMCU microcontroller as a data processor and an ultrasonic sensor as data input as well as a buzzer and servo. An ultrasonic sensor is a device that can measure the distance of an object by utilizing reflected ultrasonic waves. This water monitoring uses firebase as a database to store data. To display measurement data, the interface is also designed using the Android Studio application software in the form of graphs, pump status, and water notifications. The test results show that the ultrasonic sensor used is able to measure water levels from 2 cm to 20 cm with an average measurement error of 4.93%. Water monitoring is designed to be able to produce a system output response according to the reference value given. In addition, water level monitoring can be displayed on the Android software interactively

Measuring and calibrating of the parasitic mismatch in MEMS accelerometer based on harmonic distortion self-test

Capacitive MEMS (micro-electromechanical systems) accelerometers have inherent parasitic capacitors, the existence of which will increase the system bias-instability and non-linearity. The parasitic mismatch induced signal is indistinguishable to the sensed signal and cannot be canceled by traditional offset cancellation technique. In this paper, a method to realize the on-line measuring and calibrating of this parasitic effect is proposed, which is based on the digital harmonic distortion self-test. The effect of different parasitic mismatch on the system output response is analyzed in detail. As will show, in self-test mode, the parasitic mismatch will induce distinctive even-order harmonics. An on-chip digital self-test circuit is built to excite and measure this characteristic. According to the measured results, the parasitic mismatch of the front-end interface can be effectively tuned out whereby preserved calibrating unit. The test result shows, using proposed technique, the parasitic mismatch could be reduced in two orders of magnitude.

Separable multi‐innovation stochastic gradient estimation algorithm for the nonlinear dynamic responses of systems

SummaryThis article is concerned with the parameter identification problem of nonlinear dynamic responses for the linear time‐invariant system by means of an impulse excitation signal and discrete observation data. Using the impulse signal as the input, the impulse response experiment is carried out and the dynamical moving sampling is designed to generate the measured data for deriving new identification algorithms. By applying the moving window data that contain the dynamical information of the system to be identified, an objective function with respect to the parameters of the systems is constructed according to the impulse response. In accordance with different functional relations between the system parameters and the system output response, the unknown parameter vector of the system is separated into a linear parameter vector and a nonlinear parameter vector. Based on the separated parameter vectors, two subidentification models are constructed and a separable identification algorithm is presented through the gradient search to improve the accuracy. Moreover, for the purpose of enhancing the estimation accuracy and capturing the dynamical feature of the systems, the moving window data are employed to develop the separable identification algorithm. The performance of the proposed separable identification method is illustrated via a numerical example.

Dynamic Loading System of an Agricultural Vehicle Power Wagon Based on Complex Loading

A dynamic loading system of a power wagon is studied to solve the problems of low loading precision, slow response speed, and poor adaptive capability of a power wagon in an agricultural vehicle traction test. The load feature of an engine is analyzed under different gear locations, governor handle positions, and vehicle speeds. Meanwhile, the load feature of an eddy current retarder is examined based on a backpropagation (BP) neural network proportional-integral-derivative (PID) control algorithm. The mathematical model for the loading system is established. The time domain model for traction is refactored by a white noise filter according to the power spectral density function. To improve the dynamic loading performance of the eddy current retarder, the BP neural network PID control algorithm is used to change its exciting voltage to achieve an accurate simulation of the actual working load. The effectiveness and practicability of this control algorithm are validated by comparing its system output response dynamic characteristics with those of the traditional PID control algorithm. Simulation results show that the output error is less than 2.8% and the maximum delay is 56.5 ms when the BP neural network PID control is used. On the basis of this result, the traction property of a tractor with several wheels is tested on the road. The test and analysis results indicate that the output loads of the loading system accurately simulate the field actual load of the tractor. The maximum output load value of the system is 42-48 kN and the applicable speed range is 0-25 km/h. The output error is less than 3.5% and the maximum delay is 85.6 ms.

Performance Enhancement of Micro Grid System with SMES Storage System Based on Mine Blast Optimization Algorithm

Frequency control represents a critically significant issue for the enhancement of the dynamic performance of isolated micro grids. The micro grid system studied here was a wind–diesel system. A new and robust optimization technique called the mine blast algorithm (MBA) was designed for tuning the PID (proportional–integral–differential) gains of the blade pitch controller of the wind turbine side and the gains of the superconducting magnetic energy storage (SMES) controller. SMES was implemented to release and absorb active power quickly in order to achieve a balance between generation and load power, and thereby control system frequency. The minimization of frequency and output wind power deviations were considered as objective functions for the PID controller of the wind turbine, and the diesel frequency and power deviations were used as objective functions for optimizing the SMES controller gains. Different case studies were considered by applying disturbances in input wind, load power, and wind gust, and sensitivity analysis was conducted by applying harsh conditions with varying fluid coupling parameter of the wind–diesel hybrid system. The proposed MBA–SMES was compared with MBA (tuned PID pitch controller) and classical PI control systems in the Matlab environment. Simulation results showed that the MBA–SMES scheme damped the oscillations in the system output responses and improved the system performance by reducing the overshoot by 75% and 36% from classical and MBA-based systems, respectively, reduced the settling time by 45% compared to other systems, and set the final steady-state error of the frequency deviation to zero compared to other systems. The proposed scheme was extremely robust to disturbances and parameter variations.

Motion Transmissibility for Load Identification Based on Optimum Sensor Placement

Knowledge on loads acting on a structure is important for analysis and design. There are many applications in which it is difficult to measure directly the dynamic loads acting on a component. In such situations, it may be possible to estimate the imposed loads through a measurement of the system output response. Load identification through output response measurement is an inverse problem that is not only ill‐conditioned but, in general, leads to multiple solutions. Therefore, additional information such as the number and locations of the imposed loads must be provided ahead of time in order to allow for a unique solution. This paper focuses on cases where such information is not readily available and uses the concept of motion transmissibility for the identification of loads applied to a structure. The identification of loads through measurement of structural response at a finite number of optimally selected sensor locations is investigated. Optimum sensor locations are identified using the D‐optimal design algorithm to provide the most precise load estimates based on acceleration measurements using accelerometers. Simulation results for multi‐degree‐of‐freedom (MDOF) discrete and continuous systems are presented to illustrate the proposed technique. It is seen that the proposed approach is effective in determining not only the number of applied loads as well as their locations but also the magnitude of applied loads.

Using polynomial chaos expansion for uncertainty and sensitivity analysis of bridge structures

Abstract The quantification of the uncertainty effect of random system parameters, such as the loading conditions, material and geometric properties, on the system output response has gained significant attention in recent years. One of the well-known methods is the first-order second-moment (FOSM) method, which can be used to determine the mean value and variance of the system output. However, this method needs to derive the formulas for calculating the local sensitivity and it can only be used for systems with low-level uncertainties. Polynomial Chaos (PC) expansion is a new non-sampling-based method to evaluate the uncertainty evolution and quantification of a dynamical system. In this paper, PC expansion is used to represent the stochastic system output responses of civil bridge structures, which could be the natural frequencies, linear and nonlinear dynamic responses. The PC coefficients are obtained from the non-intrusive regression based method, and the statistical characteristic can be evaluated from these coefficients. The results from the proposed approach are compared with those calculated with commonly used methods, such as Monte Carlo Simulation (MCS) and FOSM. The accuracy and efficiency of the presented PC based method for uncertainty quantification and global sensitivity analysis are investigated. Global sensitivity analysis is performed to quantify the effect of uncertainty in each random system parameter on the variance of the stochastic system output response, which can be obtained directly from the PC coefficients. The results demonstrate that PC expansion can be a powerful and efficient tool for uncertainty quantification and sensitivity analysis in linear and nonlinear structure analysis.

Legendre orthogonal polynomials approximation of system gramians and its application to balanced truncation

ABSTRACTThe paper presents a procedure for computation of system gramians by using Legendre orthogonal polynomials approximation of the system state impulse and output responses. The proposed approach is trajectory based and relies on the system state and output trajectories snapshots selected either by experiment or computer simulation. It is defined in deterministic settings as opposed to similar approaches defined in stochastic settings by using the data covariance matrix. The advantage of using orthogonal series approximation for the gramians is to avoid solving the usual Lyapunov equations. The proposed method can be equally well applied to linear time-invariant as well as time-varying systems, and even to unstable systems, since the gramians approximation is performed on a finite interval of time. When the observation interval contains the whole energy of the system state impulse and output responses, the proposed method gives similar results as the gramians computed by solving Lyapunov equations. Several experiments are performed showing the good approximation properties of the presented method.

Multi-objective optimization of the performance-emission trade-off characteristics of a CRDI coupled CNG diesel dual-fuel operation: A GEP meta-model assisted MOGA endeavour

A meta-model based multi-objective optimization endeavor was undertaken in the present work to investigate the potential of the off-line model based calibration technique to extend the actual CRDI-CNG dual-fuel experimental investigations in determining the possibility of unearthing viable potential trade-off domains hitherto unexplored by the constraints of resources, cost and time warranted by an experimental investigation. For the ensuing optimization study, CNG energy share, fuel injection pressure and load have been used as the decision variables while PM, NHC and BSFC were chosen as the output variables to be optimized. In absence of a closed form correlation between the participating variables under study, the explicit characterization capability of the Gene Expression Programming technique was harnessed. The appropriate GEP based meta-models were adopted from a previous study correlating the identical system output responses for the same set of decision variables of interest in the present study. Genetic algorithm was chosen as the optimization routine in the present study in view of its promising potential of extremely fast convergent speed, diversity of optimal solutions and simplicity of operation. Experimental validation of the obtained solutions pertaining to the desired objectives were carried out by actual experimentation. The present optimization endeavor was able to better the best vantage in category of the desired objective of minimum fuel consumption and exhaust emissions, obtained not only as compared to baseline diesel operation comprehensively but also was superior than the actual CRDI-CNG strategy during actual dual-fuel operation corresponding to actual experimentation.

Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring

Frequency methods such as frequency spectrum analysis, frequency spike detection, demodulation, envelope spectrum method have been widely used for condition monitoring of engineering structural systems. Different from the conventional frequency methods, the transmissibility function (TF) represents the relationship between different system output responses such as, e.g., vibration and acoustic emission sensor measurements. This paper introduces a simple and effective generalized transmissibility damage indicator (GTDI) for TF-based condition monitoring. Unlike the conventional transmissibility damage indicator, the new GTDI can improve the detection sensitivity, reduces noise effects and avoid dynamic loadings effects. This is achieved by combining multiple groups of data to obtain more accurate transmissibility analysis, exploiting all the available TFs, and using multiple references. This has two advantages. First, it does not require any other priori knowledge about the system responses. Therefore, the method can be used for the condition monitoring of a wide range of components or systems. Furthermore, the method can be easily implemented using fast Fourier transform or power spectra density methods and therefore is computationally efficient. These make the method very suitable for implementing online real-time condition monitoring. The method is investigated by simulation studies and then applied to analyze the vibration data of the main bearing of operating wind turbines, producing very promising results.

A scaffold protein connects type IV pili with the Chp chemosensory system to mediate activation of virulence signaling in Pseudomonas aeruginosa.

Type IV pili (TFP) function as mechanosensors to trigger acute virulence programs in Pseudomonas aeruginosa. On surface contact, TFP retraction activates the Chp chemosensory system phosphorelay to upregulate 3', 5'-cyclic monophosphate (cAMP) production and transcription of virulence-associated genes. To dissect the specific interactions mediating the mechanochemical relay, we used affinity purification/mass spectrometry, directed co-immunoprecipitations in P. aeruginosa, single cell analysis of contact-dependent transcriptional reporters, subcellular localization and bacterial two hybrid assays. We demonstrate that FimL, a Chp chemosensory system accessory protein of unknown function, directly links the integral component of the TFP structural complex FimV, a peptidoglycan binding protein, with one of the Chp system output response regulators PilG. FimL and PilG colocalize at cell poles in a FimV-dependent manner. While PilG phosphorylation is required for TFP function and mechanochemical signaling, it is not required for polar localization or binding to FimL. Phylogenetic analysis reveals other bacterial species simultaneously encode TFP, the Chp system, FimL, FimV and adenylate cyclase homologs, suggesting that surface sensing may be widespread among TFP-expressing bacteria. We propose that FimL acts as a scaffold enabling spatial colocalization of TFP and Chp system components to coordinate signaling leading to cAMP-dependent upregulation of virulence genes on surface contact.

A New Transmissibility Analysis Method for Detection and Location of Damage via Nonlinear Features in MDOF Structural Systems

In this paper, a new transmissibility analysis method is proposed for the detection and location of damage via nonlinear features in multidegree-of-freedom (MDOF) structural systems. The method is derived based on the transmissibility of nonlinear output frequency response functions (NOFRFs), a concept recently proposed to extend the traditional transmissibility concept to the nonlinear case. The implementation of the method is only based on measured system output responses and by evaluating and analyzing the transmissibility of these system responses at super-harmonics. This overcomes the problems with available techniques, which assume there is one damaged component with nonlinear features in the system and/or require loading on inspected structural systems is measurable. Both numerical simulation studies and experimental data analysis have been conducted to verify the effectiveness and demonstrate the potential practical applications of the new method.