Real time remaining useful life prediction based on nonlinear Wiener based degradation processes with measurement errors

Real time remaining useful life(RUL) prediction based on condition monitoring is an essential part in condition based maintenance(CBM). In the current methods about the real time RUL prediction of the nonlinear degradation process, the measurement error is not considered and forecasting uncertainty is large. Therefore, an approximate analytical RUL distribution in a closed-form of a nonlinear Wiener based degradation process with measurement errors was proposed. The maximum likelihood estimation approach was used to estimate the unknown fixed parameters in the proposed model. When the newly observed data are available, the random parameter is updated by the Bayesian method to make the estimation adapt to the item's individual characteristic and reduce the uncertainty of the estimation. The simulation results show that considering measurement errors in the degradation process can significantly improve the accuracy of real time RUL prediction.

Strategy for Data Stream Processing Based on Measurement Metadata: An Outpatient Monitoring Scenario

In this work we discuss SDSPbMM, an integrated Strategy for Data Stream Processing based on Measurement Metadata, applied to an outpatient monitoring scenario. The measures associated to the attributes of the patient (entity) under monitoring, come from heterogeneous data sources as data streams, together with metadata associated with the formal definition of a measurement and evaluation project. Such metadata supports the patient analysis and monitoring in a more consistent way, facilitating for instance: i) The early detection of problems typical of data such as missing values, outliers, among others;and ii) The risk anticipation by means of on-line classification models adapted to the patient. We also performed a simulation using a prototype developed for outpatient monitoring, in order to analyze empirically processing times and variable scalability, which shed light on the feasibility of applying the prototype to real situations. In addition, we analyze statistically the results of the simulation, in order to detect the components which incorporate more variability to the system.

A beam position measurement system of fully digital signal processing at SSRF

This fully digital beam position measurement instrument is designed for beam position monitoring and machine research in Shanghai Synchrotron Radiation Facility. The signals received from four position-sensitive detectors are narrow pulses with a repetition rate up to 499.654 MHz and a pulse width of around 100 ps, and their dynamic range could vary over more than 40 dB in machine research. By the employment of the under-sampling technique based on high-speed high-resolution A/D conversion, all the processing procedure is performed fully by the digital signal processing algorithms integrated in one single Field Programmable Gate Array. This system functions well in the laboratory and commissioning tests, demonstrating a position resolution (at the turn by turn rate of 694 kHz) better than 7 μm over the input amplitude range of -40 dBm to 10 dBm which is well beyond the requirement.

Estimation of spatially distributed processes using mobile sensor networks with missing measurements

This paper investigates the estimation problem for a spatially distributed process described by a partial differential equation with missing measurements.The randomly missing measurements are introduced in order to better reflect the reality in the sensor network.To improve the estimation performance for the spatially distributed process,a network of sensors which are allowed to move within the spatial domain is used.We aim to design an estimator which is used to approximate the distributed process and the mobile trajectories for sensors such that,for all possible missing measurements,the estimation error system is globally asymptotically stable in the mean square sense.By constructing Lyapunov functionals and using inequality analysis,the guidance scheme of every sensor and the convergence of the estimation error system are obtained.Finally,a numerical example is given to verify the effectiveness of the proposed estimator utilizing the proposed guidance scheme for sensors.

Confidence Level Based on Ridge Estimator in Process Measurement and Its Application

Ordinary least squares(OLS) algorithm is widely applied in process measurement, because the sensor model used to estimate unknown parameters can be approximated through multivariate linear model. However, with few or noisy data or multi-collinearity, unbiased OLS leads to large variance. Biased estimators, especially ridge estimator, have been introduced to improve OLS by trading bias for variance. Ridge estimator is feasible as an estimator with smaller variance. At the same confidence level, with additive noise as the normal random variable, the less variance one estimator has, the shorter the two-sided symmetric confidence interval is. However, this finding is limited to the unbiased estimator and few studies analyze and compare the confidence levels between ridge estimator and OLS. This paper derives the matrix of ridge parameters under necessary and sufficient conditions based on which ridge estimator is superior to OLS in terms of mean squares error matrix, rather than mean squares error.Then the confidence levels between ridge estimator and OLS are compared under the condition of OLS fixed symmetric confidence interval, rather than the criteria for evaluating the validity of different unbiased estimators. We conclude that the confidence level of ridge estimator can not be directly compared with that of OLS based on the criteria available for unbiased estimators, which is verified by a simulation and a laboratory scale experiment on a single parameter measurement.

New Optical Triangulation and Digital Image Processing in Measurement

In this paper,a new direct optical triangulation(DOT) for measuring theout-of-plane displacement is given.In order to state its principle,DOT is used to measure a micro-displacement of a rigid body,and at the same time,the method of digital image processing is also given.

Case Depth Measurement of Induction Hardening Using Image Processing

Case depth measurement of the induction hardened steel parts is necessary for quality control. Vickers microhardness test is the most industrially accepted method to identify the case depth. But this method is a time consuming one and it requires expensive equipment. The aim of this study is to develop a different method to determine the case depth using image processing. The surface hardened steel samples were cross cut, ground and etched with Nital. The etched macrosectioned specimens were scanned by a scanner. The scanned images were evaluated by the developed software. The principle of the software is to identify the gray level difference. The effective case depths of the surface hardened specimens obtained by Vickers microhardness test and the developed method were compared. It was found that the deviation of the developed method was ±0.12 mm at the case depth range of 0.6 - 2.0 mm and mm at the case depth range of 2.1 - 4.3 mm. The measuring time was only 20% of Vickers microhardness test. The deviation range is much lower than the tolerance case depth specification for induction hardening in general.

基于．NET及Measurement Studio的USB型测试系统研制

介绍了一种基于．NET框架环境及Measurement Studio．NET的USB接口型通用多路高速并行采集测试分析系统的开发,以及针对振动设备监测的常用算法开发应用。该系统充分体现虚拟仪器的概念,以计算机为主体,以测试分析软件为核心,配备传感器、数据采集模块等外围设备,以较低的成本,完成了各种传统信号测试仪器的功能。测试系统软件建立在．NET开发平台上,以C#语言与NI公司的Measurement Studio混合编程实现测试算法及用户界面。另外,系统界面友好、操作简便、可移植性强,已在振动设备测试方面进行了初步应用。

Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Images are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

Modeling and monitoring of nonlinear multi-mode processes based on similarity measure-KPCA

A new modeling and monitoring approach for multi-mode processes is proposed.The method of similarity measure(SM) and kernel principal component analysis(KPCA) are integrated to construct SM-KPCA monitoring scheme,where SM method serves as the separation of common subspace and specific subspace.Compared with the traditional methods,the main contributions of this work are:1) SM consisted of two measures of distance and angle to accommodate process characters.The different monitoring effect involves putting on the different weight,which would simplify the monitoring model structure and enhance its reliability and robustness.2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace.Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.

Measuring the Condition of Parking Lot by Image Processing

Aim To study the parking management in the condition of vehicles' increasing. Methods The methods of pattern recognition and image processing were used to analyze the eigenvalues of parking lot images. Results The automatic identification of every parking place in the parking plot was realized. The automatic measuring of parked vehicle count and parking lot utilization was completed. Conclusion It can complete the real time recognition, and has some practicabilities.

IMAGE PROFILE AREA CALCULATION BASED ON CIRCULAR SAMPLE MEASUREMENT CALIBRATION

A practical approach of measurement calibration is presented for obtaining the true area of the photographed objects projected in the 2-D image scene. The calibration is performed using three circular samples with given diameters. The process is first to obtain the ratio mm/pixel in two orthogonal directions, and then use the obtained ratios with the total number of pixels scanned within projected area of the object of interest to compute the desired area. Compared the optically measured areas with their corresponding true areas, the results show that the proposed method is quite encouraging and the relevant application also proves the approach adequately accurate.

Large Deviation Theorem for Empirical Measures of Degenerate Diffusion Processes

A class of multi dimensional degenerate diffusion processes X ε(t) in R r(r≥2) are considered and the asymptotic properties of empirical measures are investigated; here X ε(t) saitisfies the stochastic differential equation dX ε(t)=σ(X ε(t)) d W(t)+B(X ε(t)) d t+ εσ～(X ε(t)) d W(t),ε>0. X ε(t) are small random perturbations of the degenerate diffusion process X(t), which satisfies the stochastic differential equation dX(t)=σ(X(t)) d W(t)+B(X(t)) d t. A large deviation theorem for projection measures ν on R r-n (n<r) of empirical measures μ are proved

Image-based Water Level Measurement Method under Stained Ruler

This paper propoes the water level measuring method based on the image, while the ruler used to indicate the water level is stained. The contamination of the ruler weakens or eliminates many features which are required for the image processing. However, the feature of the color difference between the ruler and the water surface are firmer on the environmental change compare to the other features. As the color differeaces are embossed, only the region of the ruler is limited to eliminate the noise, and the average image is produced by using several continuous frames. A histogram is then produced on the height axis of the produced intensity average image. Local peaks and local valleys are detected, and the section between the peak and valley which have the greatest change is looked for. The valley point at this very moment is used to detect the water level. The detected water level is then converted to the actual water level by using the mapping table. The proposed method is compared to the ultrasonic based method to evaluate its accuracy and efficiency on the various contaminated environments.

The Exact Measure Functions of the Images for a Class of Self-Similar Processes

Let X= (Ω, ■, ■_t, X_t,, θ_t, p~x) be a self-similar Markov process on (0,∞) with non-decreasing path. The exact Hausdorff and Packing measure functions of the image X([0,t] ) are obtained.

Improvement of Boxcar Integrator and Its Application to Low Resistance Measurement of Inductive Load Coil

A boxcar integrator is described which is suitable for the low-repetition-rate signal processing. This boxcar integrator, named fixed-interval mode boxcar integrator, is able to reject harmonics other than the first harmonic component. It can also decrease the effective time constant In many situations, the antialiasing filter with narrow bandwidth will cause distortion of the input signal. The fixed-interval mode boxcar integrator with suitable gate width can achieve relative high performance without signal distortion because the bandwidth of its antialiasing filter can be wider than that in the fixed-Point boxcar integrator. ms boxcar integrator is used as majn part of signalprocessing circult in the low resisance measurement of inductive load coil. The results of experiments show that the fixed-interval boxcar integrator is suitable for low-repetition-rate use.

A novel algorithm for distance measurement using stereo camera

Distance estimation can be achieved by using active sensors or with the help of passive sensors such as cameras.The stereo vision system is generally composed of two cameras to mimic the human binocular vision.In this paper,a Python-based algorithm is pro-posed to find the parameters of each camera,rectify the images,create the disparity maps and finally use these maps for distance measurements.Experiments using real-time im-ages,which were captured from our stereo vision system,of different obstacles posi-tioned at multiple distances(60-200 cm)prove the effectiveness of the proposed program and show that the calculated distance to the obstacle is relatively accurate.The accuracy of distance measurement is up to 99.83%.The processing time needed to calculate the distance between the obstacle and the cameras is less than 0.355 s.

Convergence of Invariant Measures of Truncation Approximations to Markov Processes

Let Q be the Q-matrix of an irreducible, positive recurrent Markov process on a countable state space. We show that, under a number of conditions, the stationary distributions of the n × n north-west corner augmentations of Q converge in total variation to the stationary distribution of the process. Two conditions guaranteeing such convergence include exponential ergodicity and stochastic monotonicity of the process. The same also holds for processes dominated by a stochastically monotone Markov process. In addition, we shall show that finite perturbations of stochastically monotone processes may be viewed as being dominated by a stochastically monotone process, thus extending the scope of these results to a larger class of processes. Consequently, the augmentation method provides an attractive, intuitive method for approximating the stationary distributions of a large class of Markov processes on countably infinite state spaces from a finite amount of known information.

Bunch-by-bunch beam size measurement during injection at Shanghai Synchrotron Radiation Facility

Fast beam profile measurement is important in fast beam dynamic behavior investigations. A bunch-bybunch beam size measurement system, which is presently used to measure horizontal profile, has been developed at the Shanghai Synchrotron Radiation Facility(SSRF) and is capable of measuring bunches within a separation of 2 ns.The system is based on a direct-imaging optical system and high-speed photomultiplier array detector. A high-bandwidth linear signal amplifier and acquisition module have also been designed to process bunch-by-bunch multichannel signals from the detector. The software resampling technique and principal component analysis method were developed to obtain the synchronized data and enhance the signal-to-noise ratio. The fast injection of transients was successfully captured and analyzed. Moreover, the bunchby-bunch positions and sizes exhibited strong oscillation after the injection at the horizontal betatron oscillation frequency of the SSRF storage ring, and this demonstrated the bunch-by-bunch measurement capability of our system.

Measurement of a Three－dimensional Gas Temperature Field with Holographic Interferometry

It is well known that optical tomography can accurately and quantitatively reconstruct the refractive index field of a transparent medium and display the three dimensional image of other physical quantities relevant to temperature or density. In this paper, a new multidirectional holographic interferometric system is built, and two kinds of image reconstruction algorithms are introduced and an automatic image processing system of interferogram is designed. A three dimentsional asymmetric gas flow field above a combustor is expertmentally investigated with holographic interferometry. The reconstructed temperatures are similar to those measured with a thermocouple.