光纤非本征法布里-玻罗传感器兰姆波检测灵敏度分析

基于兰姆波的结构工况检测技术在评估复合材料和金属结构的安全性和耐久性方面发挥着重要的作用。作为对传统的压电换能器（PZT）的一种很好的替代,光纤传感器在传感方面的应用正被广泛地挖掘出来,包括兰姆波检测。本文从理论上建立了超声兰姆波作用下光纤非本征法布里．玻罗（EFPI）传感器参数与其输出性能之间的关系。数值结果显示了传感器的性能与其相对于声源的方向角以及传感器的计量长度与超声波长的比值相关。所得出的结论对于EFPI传感器精确地探测兰姆波提供了理论依据。     

A vapour compression chiller fault detection technique based on adaptative algorithms. Application to on-line refrigerant leakage detection

The existence of faults in vapour compression chillers plays a significant role in terms of energy efficiency loss, performance degradations, and even environmental implications. In this paper, a dynamic model-based fault detection technique suitable for real-time implementation is proposed. The main objective is to obtain a reliable and automated tool for fault detection in vapour compression chillers, which can be applied in steady-state or transient operation. The fault detection methodology is based on comparing actual and expected performance using an adaptative model and operating variables dynamic thresholds. The technique has been successful applied for on-line refrigerant leakage detection with experimental tests involving the artificial introduction of the fault in a laboratory vapour compression plant, showing the results its capability of detecting incipient leakage failure conditions avoiding false alarms.     

Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning

The benefits of applying automated fault detection and diagnosis (AFDD) to chillers include less expensive repairs, timely maintenance, and shorter downtimes. This study employs feature selection (FS) techniques, such as mutual-information-based filter and genetic-algorithm-based wrapper, to help search for the important sensors in data driven chiller FDD applications, so as to improve FDD performance while saving initial sensor cost. The ‘one-against-one’ multi-class support vector machine (SVM) is adopted as a FDD tool. The results show that the eight features/sensors, centered around the core refrigeration cycle and selected by the GA-SVM wrapper from the original 64 features, outperform the other three feature subsets by the GA-LDA (linear discriminant analysis) wrapper, with an overall classification correct rate (CR) as high as 99.53% for the 4000 test samples randomly covering the normal and seven typical faulty modes. The CRs for the four cases with FS are all higher than that without FS (97.45%) and the test time is much less, about 28-36%. The FDD performance for normal or each of the faulty modes is also evaluated in details in terms of hit rate (HR) and false alarm rate (FAR).     

Nuclear power plant sensor fault detection using singular value decomposition-based method

In a nuclear power plant, periodic sensor calibration is necessary to ensure the correctness of measurements. Those sensors which have gone out of calibration can lead to malfunction of the plant, possibly causing a loss in revenue or damage to equipment. Continuous sensor status monitoring is desirable to assure smooth running of the plant and reduce maintenance costs associated with unnecessary manual sensor calibrations. In this paper, a method is proposed to detect and identify any degradation of sensor performance. The validation process consists of two steps: (i) residual generation and (ii) fault detection by residual evaluation. Singular value decomposition (SVD) and Euclidean distance (ED) methods are used to generate the residual and evaluate the fault on the residual space, respectively. This paper claims that SVD-based fault detection method is better than the well-known principal component analysis-based method. The method is validated using data from fast breeder test reactor.     

基于模糊推理系统的制冷机组故障检测与诊断

设计制冷机组故障检测与诊断的模糊推理系统，并用实验数据对此系统进行检验。研究结果表明，利用模糊推理系统进行制冷机组故障检测与诊断是可行的。     

Si-based sensor for virus detection

This paper describes the development of a sensor for the detection of viruses. The detection scheme uses sense and dummy capacitors and a sense amplifier circuit to compare capacitance values. The presence of sufficient biological material within the sense capacitor alters the sense amplifier output. This promising approach has the potential to more rapidly test for the presence of virus using an IC platform.     

Model-based methods for textile fault detection

Addressing the problem of automatic fault detection in woven and dyed fabric, we discuss a number of new statistical model-based methods and relate them to a first stage of point/local detection and a second stage of extended pattern detection. One model-based method defines a maximum likelihood binarization of the image. In another model-based method, we describe a discrete Fourier transform-based texture analysis technique that is highly effective for woven textiles in discriminating subtle flaw patterns from the pronounced background of repetitive weaving pattern and random clutter. Finally, we describe a model-based clustering method that can be employed to aggregate perceptual groupings of point and local detections. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 339–346, 1999     

溴化锂吸收式制冷机换热管腐蚀失效分析

对某制冷机组吸收器的冷却水管出现不同程度的腐蚀穿孔进行宏观和微观形貌分析，对腐蚀孔附近腐蚀产物进行扫描电镜-能谱分析，并对冷却水管化学成分、冷却水质和溴化锂溶液进行分析。指出吸收器壳体开裂导致大量空气进入溴化锂水溶液，产生点蚀源，铜管的化学成分为点蚀的产生提供了材料条件，冷却水硬度超标加剧了铜管点腐蚀。     

并联涡旋式压缩机在空气源热泵机组中的应用分析

结合并联涡旋式压缩机的结构特点,介绍涡旋式压缩机并联使用的技术优势;对空气源并联涡旋式热泵机组进行试验研究,并与同冷量的空气源螺杆式热泵机组进行对比。试验发现,空气源并联涡旋式热泵机组具有更高的能效比。最后,给出并联机油气平衡问题的解决措施。     

A multivariate parameter trace analysis for online fault detection in a semiconductor etch tool

The objective of this paper is to develop a wafer-by-wafer fault detection model for a semiconductor etch tool operating in a worksite situation in which the tool parameter traces are correlated and drift slowly from an initial recipe setting. Process drift is a common occurrence in many processes because of the aging of tool components. The proposed fault detection model compares the entire trace structures of the tool parameters with reference templates by using an improved DTW (dynamic time warping) algorithm, and it performs a T?²-based multivariate analysis with the structure similarity scores created by the improved DTW. In addition, to adapt to the process drift, a recursive T?² update procedure with an optimal correction factor is incorporated in the model. The optimal correction factor is derived using the Kalman filtering technique. Experiments using the data collected from a worksite reactive ion etching process demonstrate that the performance of the proposed fault detection model is very encouraging.     

PCA-based feature selection scheme for machine defect classification

The sensitivity of various features that are characteristic of a machine defect may vary considerably under different operating conditions. Hence it is critical to devise a systematic feature selection scheme that provides guidance on choosing the most representative features for defect classification. This paper presents a feature selection scheme based on the principal component analysis (PCA) method. The effectiveness of the scheme was verified experimentally on a bearing test bed, using both supervised and unsupervised defect classification approaches. The objective of the study was to identify the severity level of bearing defects, where no a priori knowledge on the defect conditions was available. The proposed scheme has shown to provide more accurate defect classification with fewer feature inputs than using all features initially considered relevant. The result confirms its utility as an effective tool for machine health assessment.     

Thermal luminescence sensor for ground-path contamination detection

A standoff method of detecting liquids on terrestrial and synthetic landscapes is presented. The interstitial liquid layers are identified through their unique molecular vibration modes in the 7.14-14.29-microm middle infrared (fingerprint) region of liberated thermal luminescence. Several seconds of 2.45-GHz beam exposure at 1.5 W cm(-1) is sufficient for detecting polydimethyl siloxane lightly wetting the soil through its fundamental Si-CH3 and Si-O-Si stretching modes in the fingerprint region. A detection window of thermal opportunity opens as the surface attains maximum thermal gradient following irradiation by the microwave beam. The contaminant is revealed inside this window by means of a simple difference-spectrum measurement. Our goal is to reduce the time needed for optimum detection of the contaminant's thermal spectrum to a subsecond exposure from a limited intensity beam.     

Tribotronic transistor sensor for enhanced hydrogen detection

Hydrogen detection with a high sensitivity is necessary for preventing potential explosions and fire.In this study,a novel ZnO tribotronic transistor is developed by coupling a ZnO field effect transistor (FET) and triboelectric nanogenerator in free-standing mode and is used as a sensor for hydrogen detection at room temperature.Tribotronic modulated performances of the hydrogen sensor are demonstrated by investigating its output characteristics at different sliding distances and hydrogen concentrations.By applying an external mechanical force to the device for sliding electrification,the detection sensitivity of the ZnO tribotronic transistor sensor is improved,with a significant enhancement achieved in output current by 62 times at 500 ppm hydrogen and 1 V bias voltage.This study demonstrates an extension of the applications of emerging tribotronics for gas detection and a prospective approach to improve the performance of the hydrogen sensor via human-interfacing.     

Interferometric sensor for detection of surface-bound bioreactions

An integrated optical interferometer for direct detection of affinity reactions is presented. A modern version of a Young's interferometer is built with a waveguide structure as beam splitter and as sensing element. Resistive waveguides were produced by plasma-enhanced chemical vapor deposition of silicon oxinitride. At the output of this device a fringe pattern is detected by a CCD line camera. The adsorption of molecules on top of the waveguides is observed with a detection limit of 750 fg/mm(2). The resolvable variation of effective refractive index is 9 x 10(-8).     

Miniaturization of EISCAP sensor for triglyceride detection

In this paper we discuss the fabrication and characterization of miniaturized triglyceride biosensors on crystalline silicon and porous silicon (PS) substrates. The sensors are miniaturized Electrolyte Insulator Semiconductor Capacitors (mini-EISCAPs), which primarily sense the pH variation of the electrolyte used. The lipase enzyme, which catalyses the hydrolysis of triglycerides, was immobilized on the sensor surface. Triglyceride solutions introduced into the enzyme immobilized sensor produced butyric acid which causes the change in pH of the electrolyte. Miniaturized EISCAP sensors were fabricated using bulk micromachining technique and have silicon nitride as the pH sensitive dielectric layer. The sensors are cubical pits of dimensions 1,500 microm x 1,500 microm x 100 microm which can hold an electrolyte volume of 0.1 microl. The pH changes in the solution can be sensed through the EISCAP sensors by monitoring the flatband voltage shift in the Capacitance-Voltage (C-V) characteristics taken during the course of the reaction. The reaction rate is found to be quite high in the miniature cells when compared to the sensors of bigger dimensions.     

Miniaturization of EISCAP sensor for triglyceride detection

Designing a three-dimensional (3-D) ideal scaffold has been one of the main goals in biomaterials and tissue engineering, and various mechanical techniques have been applied to fabricate biomedical scaffolds used for soft and hard tissue regeneration. Scaffolds should be biodegradable and biocompatible, provide temporary support for cell growth to allow cell adhesion, and consist of a defined structure that can be formed into customized shapes by a computer-aided design system. This versatility in preparing scaffolds gives us the opportunity to use rapid prototyping devices to fabricate polymeric scaffolds. In this study, we fabricated polycaprolactone scaffolds with interconnecting pores using a 3-D melt plotting system and compared the plotted scaffolds to those made by salt leaching. Scanning electron microscopy, a laser scanning microscope, micro-computed tomography, and dynamic mechanical analysis were used to characterize the geometry and mechanical properties of the resulting scaffolds and morphology of attached cells. The plotted scaffolds had the obvious advantage that their mechanical properties could be easily manipulated by adjusting the scaffold geometry. In addition, the plotted scaffolds provided more opportunity for cells to expand between the strands of the scaffold compared to the salt-leached scaffold.     

A carbon-nanotube-based sensor array for formaldehyde detection

We have fabricated a sensor array consisting of 32 sensor elements with pristine, doped and metal-loaded single-wall carbon nanotubes as sensing materials. The sensor elements consist of interdigitated electrodes with varying finger widths and gaps. The chemiresistor-type sensors provide a significant response to formaldehyde at concentrations down to 10 ppb in air with rapid response and recovery times.     

Sensitivity analysis for pallet loading

Summary The problem of optimising layout patterns for palletisation has been widely studied, but the sensitivity of these solutions to changes in both pallet size and pack dimensions has received little attention. This is in spite of the fact that a small amount of overhang, a minor product dimensional change, or slightly thinner packaging may provide significant improvements. This paper examines ways in which such problems may be investigated and includes sensitivity analyses of pack and pallet dimensions as well as more complex investigations such as the sensitivity to pack volumetric changes.