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Traditional vision measurement algorithms have many shortcomings, such as complex process, multi-parameter adjustment and the integer pixel accuracy. To solve these problems, a sub-pixel displacement measurement algorithm based on Taylor expansion is proposed. The displacement vector between adjacent frames is modeled in the first step, and then shift equation of the template image is constructed based on assumptions of constant brightness and spatial consistency. After performing a first-order Taylor series approximation, the displacement vector is obtained by least squares estimation. Finally, through iteratively updating the position of template image, displacement is rapidly extracted. In order to verify the accuracy and effectiveness of the Taylor expansion algorithm, the accuracy test of the grating ruler platform in the presence of the target and the vibration measurement experiment on vibration motor in the absence of the target are designed. Experimental results show that, compared with template matching algorithm based on normalized cross correlation, for the experiments mentioned in this paper, the proposed algorithm can control the calculation time for one frame in less than 1 ms, and achieve higher sub-pixel computation accuracy. Compared with the actual set frequency of 10.78 Hz, the measured frequency of the motor is very consistent with a relative error of 1.1%. It also verifies that the Taylor expansion algorithm proposed in this paper is a reliable and effective non-contact measurement method with important practical application value for vibration measurement.

Wayside acoustic detection is a promising technology for railway-vehicle bearing health monitoring due to its merits of non-conduct measurement, low cost, and early warning capacity. However, the diagnostic accuracy will be reduced by the problems of strong background noise and Doppler distortion. Considering the super spatial directivity ability of the microphone array, in this paper, a uniform rectangular array (URA) and an optimal spatial filter (OSF) based on the principle of minimum variance distortion-less response (MVDR) are designed to improve the diagnostic accuracy. Compared with the traditional single microphone and linear array, spatial directivity can be improved significantly so that the better anti-noise performance and higher diagnostic accuracy can be achieved. First, a URA consisting of 15 microphone elements arranged into five columns and three rows is designed to capture the wayside acoustic signal. Second, the direction angle of the target moving sound source with high accuracy is calculated at different times. Third, an OSF based on the principle of the MVDR is designed to extract the target sound source signal. Fourth, Doppler effect embedded in the filtered signal is eliminated using the MVDR spectrum estimation and resampling method. Finally, the diagnosis decision is made through an envelope spectrum analysis. The comparative simulation and experimental case studies are carried out to verify the effectiveness and improvement of the proposed method.

At present, most coal-fired units use selective catalytic reduction of (SCR) and selective non-catalytic reduction of (SNCR) denitrification technology for NOx removal. Accurate prediction of NOx emission concentration of coal-fired units will not only help to further improve the regulation quality of denitrification control system, but also evaluate whether the data collected in the current site are true and accurate, and provide a basis for environmental protection departments to supervise and enforce the law of NOx emission from power plants. In this paper, based on the historical operation data of a 200MW circulating fluidized bed unit, by analyzing the factors affecting the NOx emission concentration, firstly, the correlation coefficient method is used to analyze the delay between each factor and the NOx emission concentration, then the BP neural network is used to model the two-stage intersection, the established NOx emission concentration prediction model is compared, and a more accurate NOx emission concentration prediction model is selected. Finally, the factors affecting NOx emission concentration are selected from a more accurate model. The results show that the root mean square error of the first kind of modeling is 0.023 less than that of the second kind of modeling, so the six input factors in the first kind of model are regarded as the best factors affecting the NOx emission concentration. The selected factors can be used to accurately predict the NOx emission concentration for a period of time in the future, which lays a foundation for more accurate control of SNCR denitrification control system.

In the process of a thermal power generation, the prediction of the amount of NOx in contaminant has a positive effect on the elimination of NOx. In this paper, the nonlinear moving average (MA) model with time-delays is used as the prediction model to predict the process of NOx generation. Firstly, the correlation coefficient method is used to divide all variables affecting the NOx generation into several categories, and the variable whose correlation coefficient with NOx is the biggest in each category is selected as a main variable. Then BP neural network method is used to select the three variables with the greatest influence among the main variables as the input variables in the prediction model. Next, the correlation coefficient method is used to determine the time-delay parameters of the three input variables in the prediction model. What’s more, the least square method is used to estimate other parameters of the MA model to obtain a prediction model of a NOx generation process. Finally, the practical data from the generation process of a power plant are used to verify the effectiveness of the proposed prediction method.

Traditional template matching-based motion estimation is a popular but time-consuming method for vibration vision measurement. In this study, the particle swarm optimization (PSO) algorithm is improved to solve this time-consumption problem. The convergence speed of the algorithm is increased using the adjacent frames search method in the particle swarm initialization process. A flag array is created to avoid repeated calculation in the termination strategy. The subpixel positioning accuracy is ensured by applying the surface fitting method. The robustness of the algorithm is ensured by applying the zero-mean normalized cross correlation. Simulation results demonstrate that the average extraction error of the improved PSO algorithm is less than 1%. Compared with the commonly used three-step search algorithm, diamond search algorithm, and local search algorithm, the improved PSO algorithm consumes the least number of search points. Moreover, tests on real-world image sequences show good estimation accuracy at very low computational cost. The improved PSO algorithm proposed in this study is fast, accurate, and robust, and is suitable for plane motion estimation in vision measurement.

In view of the improvement of NOX emission control requirements for coal-fired units, traditional PID controllers have been unable to effectively control large delay, large inertia, nonlinear, time-varying SNCR denitration systems, and most advanced control algorithms are often object-based models. Therefore, a model of SNCR denitration system based on adaptive weight particle swarm optimization algorithm is established. A 2×200MW heating steam turbine generator set equipped with a 2×705t/h circulating fluidized bed boiler was used as the test unit, and the selective non-catalytic reduction technology denitration system was analysed. The particle swarm optimization algorithm with adaptive weights was used to model the relationship between the urea flow rate and the NOX concentration at the chimney outlet of the SNCR denitration system under working conditions of 140 MW, 170 MW and 200 MW, respectively, to provide a process for the automatic control of the SNCR denitration systemmodel. Apply the actual data from the field to verify the model. The results show that the error between the output of the model and the actual operational data is within the allowable range, which verifies the validity of the model. This result opens up a new path for the particle swarm optimization algorithm to model the SNCR denitration process, and promotes the application of intelligent algorithms in other industrial processes.

This note reports an efficient singular value decomposition (SVD)-based vibration extraction approach that recovers sound information in silent high-speed video. A high-speed camera of which frame rates are in the range of 2 kHz–10 kHz is applied to film the vibrating objects. Sub-images cut from video frames are transformed into column vectors and then reconstructed to a new matrix. The SVD of the new matrix produces orthonormal image bases (OIBs) and image projections onto specific OIB can be recovered as understandable acoustical signals. Standard frequencies of 256 Hz and 512 Hz tuning forks are extracted offline from their vibrating surfaces and a 3.35 s speech signal is recovered online from a piece of paper that is stimulated by sound waves within 1 min.

A dual stage tuning fork transducer (DSTFT) is designed as a stator for a resonant-type inertial linear motor. The first- and second-layer resonant frequencies of DSTFT are automatically adjusted with a ratio of 1:2 by using an ANSYS optimization design algorithm, and a resonant-type sawtooth-shaped mechanical waveform is generated by composing the two resonant vibrations of DSTFT. An inertial linear motor prototype is fabricated and tested. Experimental results confirmed the effectiveness of the designed transducer. The no-load maximum speed is 21.5 mm/s with a driving voltage of 67.2 Vp-p at a base frequency of 2831 Hz. The linear speed is 10.5 mm/s, and the drag load is 0.02 N at a preload force of 1 N and a driving voltage of 114 Vp-p for the base frequency. The movement direction could be reversed by changing the driving voltage phase.

In this study, a high-speed camera system is developed to complete the vibration measurement in real time and to overcome the mass introduced by conventional contact measurements. The proposed system consists of a notebook computer and a high-speed camera which can capture the images as many as 1000 frames per second. In order to process the captured images in the computer, the normalized cross-correlation (NCC) template tracking algorithm with subpixel accuracy is introduced. Additionally, a modified local search algorithm based on the NCC is proposed to reduce the computation time and to increase efficiency significantly. The modified algorithm can rapidly accomplish one displacement extraction 10 times faster than the traditional template matching without installing any target panel onto the structures. Two experiments were carried out under laboratory and outdoor conditions to validate the accuracy and efficiency of the system performance in practice. The results demonstrated the high accuracy and efficiency of the camera system in extracting vibrating signals.

Vibration measurement is important for understanding the behavior of engineering structures. Unlike conventional contact-type measurements, vision-based methodologies have attracted a great deal of attention because of the advantages of remote measurement, nonintrusive characteristic, and no mass introduction. It is a new type of displacement sensor which is convenient and reliable. This study introduces the singular value decomposition (SVD) methods for video image processing and presents a vibration-extracted algorithm. The algorithms can successfully realize noncontact displacement measurements without undesirable influence to the structure behavior. SVD-based algorithm decomposes a matrix combined with the former frames to obtain a set of orthonormal image bases while the projections of all video frames on the basis describe the vibration information. By means of simulation, the parameters selection of SVD-based algorithm is discussed in detail. To validate the algorithm performance in practice, sinusoidal motion tests are performed. Results indicate that the proposed technique can provide fairly accurate displacement measurement. Moreover, a sound barrier experiment showing how the high-speed rail trains affect the sound barrier nearby is carried out. It is for the first time to be realized at home and abroad due to the challenge of measuring environment.

This paper addresses on the problem of vibration suppression of the Forklift Steering System by active means, which is also proved by the field test Based solely upon the measured vibration data and the Pro/Engineer and Pro/Mechanic finite element modal analysis, there is no need to evaluate or know the system parameters such as mass, damping and stiffness terms. One only need to know the one-to-one correspondence between eigenvalues and a few specific structure parts. Structure modification is equivalent to system eigenvalues changing. Using the parametric design and partial pole placement method, a few undesired eigenvalues of matrix are reassigned suitably in condition of keeping the other eigenvalues unchanged. Then we can assign the resonance peak to a prescribed value. As a result, the vibration amplitude is effectively suppressed and the dynamic characteristics are obviously improved.

This article describes the application of the receptance method vibration control technique on the steering wheel system of a forklift. Numerous advantages of the receptance method over conventional matrix methods as there is no need to know or to evaluate the M, K, C matrix since the determinant of the controller gains are expressed in terms of the receptance measured through experiment. With this method, several poles in the steering wheel system of a forklift which may produce resonance at idle speed of the engine were replaced to predetermined value. Model computation and simulation were carried out with PRO/E software and controllers were implemented using Matlb/Simulink. Locations of the open- and closed-loop poles in the complex plane are obtained to verify that the required assignment of poles had taken place.

A novel, bio-inspired, single-phase driven piezoelectric linear motor (PLM) using an asymmetric stator was designed, fabricated, and tested to avoid mode degeneracy and to simplify the drive mechanism of a piezoelectric motor. A piezoelectric transducer composed of two piezoelectric stacks and a displacement amplifier was used as the driving element of the PLM. Two simple and specially designed claws performed elliptical motion. A numerical simulation was performed to design the stator and determine the feasibility of the design mechanism of the PLM. Moreover, an experimental setup was built to validate the working principles, as well as to evaluate the performance, of the PLM. The prototype motor outputs a no-load speed of 233.7 mm/s at a voltage of 180 Vp-p and a maximum thrust force of 2.3 N under a preload of 10 N. This study verified the feasibility of the proposed design and provided a method to simplify the driving harmonic signal and structure of PLMs.

The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.