Online Monitoring

A disposable single use polymer microfluidics chip has been developed and manufactured by micro injection molding. The chip has the same outer dimensions as a standard microscope slide (25 3 76 3 1.1 mm) and is designed to be compatible with existing microscope slide handling equipment like microarray scanners. The chip contains an inlet, a 10 mL hybridization chamber capable of holding a 1000 spot array, a waste chamber and a vent to allow air to escape when sample is injected. The hybridization chamber ensures highly homogeneous hybridization conditions across the microarray. We describe the use of this chip in a flexible setup with fluorescence based detection, temperature control and liquid handling by computer controlled syringe pumps. The chip and the setup presented in this article provide a powerful tool for highly parallel studies of kinetics and thermodynamics of duplex formation in DNA microarrays. The experimental setup presented in this article enables the on-chip microarray to be hybridized and monitored at several different stringency conditions during a single assay. The performance of the chip and the setup is demonstrated by on-line measurements of a hybridization of a DNA target solution to a microarray. A presented numerical model indicates that the hybridization process in microfluidic hybridization assays is diffusion limited, due to the low values of the diffusion coefficients D of the DNA and RNA molecules involved.

The use of statistical and empirical models for the prediction of surface roughness always lacks generalizability and is not reliable when applied to unseen datasets. To increase generalizability and accuracy in predicting responses, researchers developed artificial intelligence and machine learning. The objective of this research is to develop advanced machine learning to predict surface roughness. For instance, the decision tree (DT), adaptive boosting (ADB), gradient boosting (GB), and extreme gradient boosting (XGB)s are used to predict surface roughness. The extreme gradient boosting (XGB) outperforms the other models by scoring the highest regression coefficient (R 2 = 98.7%) and least mean absolute error (MAE = 7.4%), least mean absolute percentage error (MAPE = 3.58%), and least root mean square error (RMSE = 8.0%). Further, based on the SHAP, the coolants have higher effects on surface roughness followed by cutting speed and feed rates respectively, and the tool overhangs have the least effects. Increasing cutting speed and coolant, decreases surface roughness while increasing feed rate and tool overhangs decreases surface roughness. The extreme gradient boosting (XGB) has highest generalizability and can be applied to every section of industrial and manufacturing sector for prediction of surface roughness; thus, it can save time and resource.

We consider the problem of online monitoring of transaction instances in enterprise environments, based on footprints left by the instances in system logs and using a state-based reference model of the transaction. Unlike existing approaches, we do not rely on any platform-specific knowledge, neither do we assume footprints to carry correlating identifiers, as injected through instrumentation. We outline a solution for tracking transaction instances at individual and aggregate levels, present preliminary results on theoretical analysis of monitoring precision and conclude with directions of ongoing and future research.

We consider the problem of online monitoring of transaction instances in enterprise environments, based on footprints left by the instances in system logs and using a state-based reference model of the transaction. Unlike existing approaches, we do not rely on any platform-specific knowledge, neither do we assume footprints to carry correlating identifiers, as injected through instrumentation. We outline a solution for tracking transaction instances at individual and aggregate levels, present preliminary results on theoretical analysis of monitoring precision and conclude with directions of ongoing and future research.

In recent years, IoT platforms have become increasingly used due to their untapped potential. This paper aims to create an IoT system to monitor temperature and humidity in an enclosure The Raspberry Pi 4 SBC (Single-Board Computer) development board and ThingSpeak cloud platform will be used to make this system. Data from the DHT11 humidity and temperature sensor will be collected by the Raspberry PI 4 SBC development board, which will transmit it via the WiFi connection to the IoT ThingSpeak platform cloud for further analysis. The IoT ThingSpeak platform provides data storage, processing and visualization services.

We exhibit an option and more adaptable approach that amplifies client utility by fulfilling all clients. It does this while minimizing the utilization of framework assets. We examine the profits of this last approach and create a versatile checking result Satisfy User Profiles (Sups). Through formal dissection, we distinguish sufficient optimality conditions for SUP. Utilizing genuine (RSS channels) and manufactured follows, we exactly break down the conduct of SUP under shifting conditions. Our examinations demonstrate that we can attain a high level of fulfillment of client utility when the estimations of SUP nearly gauge the genuine occasion stream, and can possibly spare a lot of framework assets. We further demonstrate that SUP can misuse criticism to enhance client utility with just a moderate build in asset use.

This article analyzes power transformers and their components, types of damage, factors causing them. The advantage of this review article is that it was initially conducted a theoretical analysis based on published articles on power transformer damage in recent years. Then a statistical analysis was carried out on damaged power transformers in real condition. In the theoretical analysis, the articles published in the databases in recent years were first identified by keywords, and then sorted according to their relevance to the topic. A statistical examination of the damaged power transformers was performed utilizing the theoretical approach. According to the results of the analysis, damage to power transformers in 6(10) kV networks occurs mainly in 100 kVA, 160 kVA and 250 kVA power transformers. One of the factors that cause the power transformer to fail is the irregular implementation of restrictions on the power supply to electrical consumers. And these failures mainly damage the windings of the power transformer. We hope that the materials in this analytical article will serve as a crucial resource.

Dissolved oxygen, pH and cell concentration have been online monitored in cultivation processes with Escherichia coli by using a MABOOMS TM (microplate-based bioreactor with optical online monitoring systems). Fluorescent sensing membranes containing Ru (dpp) 3 2+ or HPTS were prepared with GA sol-gel matrix and coated into a well of a 24-well microplate. Fluorescence intensity was measured and correlated to the dissolved oxygen or pH. Cell concentrations were also online monitored by measuring optical reflectance at 650 nm. A well of a 24-well microplate could also be divided into 4 parts, each of which was coated with fluorescent sensing membranes for the detection of dissolved oxygen or pH. The 24-well microplate coated with fluorescent sensing membranes or a 4-divided sensing membrane. was used to online monitor the dissolved oxygen, pH and cell concentration during E. coli cultivations. The online monitoring results showed the characteristics of cell growth in cultivation processes very well.

Partial discharge (PD) measurements have proved their reliability for health monitoring of insulation systems in power system components including synchronous generators, power transformers, switchgear and cables etc. Online condition monitoring and pro-active detection of PD faults have been highly demanded over the last two decades. This paper provides results from a research project to develop advanced non-intrusive sensing technologies that are cost effective, reliable and efficient for early detection of PD faults in medium voltage (MV) and high voltage (HV) air-insulated switchgear. Three sensors (high frequency E-field (D-dot) sensor, Rogowski coil and loop antenna) have been developed and tested under various PD faults and their performance were evaluated in comparison with high frequency current transformer (HFCT) which is being used commercially for PD testing and measurement. Among these three sensors, it is shown that D-dot sensor and Rogowski coil are more dependable wh...

In this paper, a capacitive sensor based on printed circuit board was designed and fabricated to detect air bubbles that appear in the engine lubricating oil. A three-electrode capacitive sensor structure is designed and simulated for monitoring and estimating amount and size of air bubbles in oil. The capacitive sensor consists of three electrodes that are structured by the PCB, copper sheets and vias. The oil pipe as a fluidic channel is threaded through the hole of capacitive sensor. By using that structure, air bubble inside fluidic channel can be detected in realtime monitoring. Simulations showing the change of signal in correspondence to the volume of air bubble inside oil channels are compared to the measurement to give a good idea of fabrication structure. In this measurement, this capacitive sensor can monitor an air bubble with a small size of 0.1 mm 3 to 3.83 mm 3 . The occurring of multi air bubbles is also monitored by this capacitive sensor for distinguishing each bub...

The online and accurate monitoring of drinking water supply networks is critically in demand to rapidly detect the accidental or deliberate contamination of drinking water. At present, miniaturized water quality monitoring sensors developed in the laboratories are usually tested under ambient pressure and steady-state flow conditions; however, in Water Distribution Systems (WDS), both the pressure and the flowrate fluctuate. In this paper, an interface is designed and fabricated using additive manufacturing or 3D printing technology-material extrusion (Trade Name: fused deposition modeling, FDM) and material jetting-to provide a conduit for miniaturized sensors for continuous online water quality monitoring. The interface is designed to meet two main criteria: low pressure at the inlet of the sensors and a low flowrate to minimize the water bled (i.e., leakage), despite varying pressure from WDS. To meet the above criteria, a two-dimensional computational fluid dynamics model was used to optimize the geometry of the channel. The 3D printed interface, with the embedded miniaturized pH and conductivity sensors, was then tested at different temperatures and flowrates. The results show that the response of the pH sensor is independent of the flowrate and temperature. As for the conductivity sensor, the flowrate and temperature affect only the readings at a very low conductivity (4 µS/cm) and high flowrates (30 mL/min), and a very high conductivity (460 µS/cm), respectively.

Diagnosis of stator current has been presented by several authors for identifying various bearing and rotor faults in an induction motor. However, there is very limited literature covering the effect of interturn faults on the stator current pattern. This is due to the reason that the effect of motor interturn insulation faults in the initial stages being not discernable and any changes in the capacitive current components are negligibly very small and of very high frequency content. This paper deals with a simple technique, utilizing the effect of the interturn fault in modifying the high frequency components of the applied pulse width modulated (PWM) voltage. Insulation Resistance (IR) and Polarization Index (PI) are two universally accepted diagnostic tests for insulation tests. The change in insulation strength between the turns affects the capacitive component of the stator line current. Resulting changes in wave shapes of applied voltage have been studied with respect to both the distance of interturn faults from line end and reduction in the insulation strength; and hence in the insulation resistance value. The studies have been made by computer simulation and validated by experiments. There is ample evidence that an impending and progressing interturn fault can be identified in Adjustable Speed Drives driven by frequency converters by studying line end coil voltage waveforms.

The Radio Frequency Identification (RFID) system is a widely used Auto-ID technology today to identify and track objects and people in manufacturing, inventory management, retailing, and security applications. The ZigBee technology, which is a network communication protocol based on the IEEE 802.15.4 standard, provides a selforganized mesh network topology with a power-effective, low data rate and multi hop data transmission. We can achieve more efficiency by introducing the ZigBee technology into the existing RFID architectures, such as having extended effective range, improving network flexibility and having compatibility with other ZigBee enabled environment systems. In this paper, we propose a system to track the location of a person using RFID (Radio Frequency Identification) Technology and notify it to a monitoring PC using wireless ZigBee Technology.

The manner in which a prognostics problem is framed is critical for enabling its solution by the proper method. Recently, data-driven prognostics techniques have demonstrated enormous potential when used alone, or as part of a hybrid solution in conjunction with physics-based models. Historical maintenance data constitutes a critical element for the use of a data-driven approach to prognostics, such as supervised machine learning. The historical data is used to create training and testing data sets to develop the machine learning model. Categorical classes for prediction are required for machine learning methods; however, faults of interest in US Army Ground Vehicle Maintenance Records appear as natural language text descriptions rather than a finite set of discrete labels. Transforming linguistically complex data into a set of prognostics classes is necessary for utilizing supervised machine learning approaches for prognostics. Manually labeling fault description instances is effec...

Virgo is an experiment aiming at the detection of gravitational waves emitted by astrophysical sources. Its detector, based on a 3km arms interferometer, is a complex setup which requires several digital control loops running up to 10kHz, an accurate and reliable central timing system and an efficient data acquisition, all of them being distributed over 3km. We overview here the main hardware and software components developed for the data acquisition system (DAQ) and its current architecture. Then, we briefly discuss its connections with interferometer's controls, especially through the automation of the interferometer's startup procedure. Then, we describe the tools used to monitor the DAQ and the performances we measured with them. Finally, are described also the tools developped for the online detector monitoring, mandatory complement of the DAQ for the commissioning of the Virgo detector.

A breakdown of the electrical insulation system causes catastrophic failure of the electrical machine and brings large process downtime losses. To determine the conditions of the stator insulation system of motor drive systems, various testing and monitoring methods have been developed. This paper presents an in-depth literature review of more than 20 existing methods, including the most common methods to assess the phase-to-ground, phase-to-phase, and turn-to-turn insulation conditions. The methods are categorized into as online and offline methods, each of which are further grouped into specific areas according to their physical nature. The paper focuses on turn-to-turn insulation testing and monitoring of low-voltage machines, which is a rapidly expanding area for both research and product development efforts. Finally, a new approach to online monitoring of turn-to-turn insulation faults for low-voltage induction motors is recommended.

This article presents a PMU placement methodology for voltage stability monitoring in Electrical Power Systems (EPSs). This methodology identifies the nodes and areas that are most sensitive to voltage stability problems, to locate the PMUs and to guarantee the voltage stability monitoring. Unlike other studies, this methodology considers the EPS as a whole and under N-1 contingencies. Criteria such as voltage stability margins, V-Q sensitivity analysis, modal analysis with participation factors in nodes, lines and generators under normal operating conditions and critical contingencies were used to identify the nodes to be installed with the PMUs. The methodology was applied to the New England 39-node test system, and the results show that this methodology is appropriate for the identification of areas and nodes that are sensitive to voltage stability problems and that these are the EPS points that should be considered for the installation of the PMUs.

A power system is subject to events that may lead to voltage stability problems, which eventually may lead to black-outs. On-line phasor measurements may allow anticipating situations leading to voltage instabilities; these on-line measurements can be accomplished by using Phasor Measurement Units (PMU). However, for a power system it is is not economical or necessary to install PMU at each bus; in order to avoid this situation it is required to develop a strategy for PMU placement that is useful and convenient, with a limited number of available PMU. This paper presents a review and comparison of some methods for PMU placement in power systems; also, we include a classification according to the type of observability obtained and consider the application of the method . Finally, a method for monitoring voltage stability in power systems is chosen and tested on the IEEE39-bus system using Matlab-PSAT (Power System Analysis Toolbox).

We present first neutrino induced events observed with a deep underwater neutrino telescope. Data from 70 days effective life time of the BAIKAL prototype telescope NT-96 have been analyzed with two different methods. With the standard track reconstruction method, 9 clear upward muon candidates have been identified, in good agreement with 8.7 events expected from Monte Carlo calculations for atmospheric neutrinos. The second analysis is tailored to muons coming from close to the opposite zenith. It yields 4 events, compared to 3.5 from Monte Carlo expectations. From this we derive a 90% upper flux limit of 1.1 • 10 -13 cm -2 sec -1 for muons in excess of those expected from atmospheric neutrinos with zenith angle > 150 degrees and energy > 10 GeV.

The Internet of Things (IoT) can reduce manufacturing cost, improve uptime, and help operators gain process insights. In this paper, we develop a system architecture for an IoT-enabled manufacturing system and demonstrate it on a bench-top milling machine. We explore on how we can build such a laboratory-scale system with commonly available, low-cost electronic sensors. Factors for designing such a system have been explored. The designed manufacturing system can take manufacturing inputs as G-Codes from the internet via a website as well as stream the data from several sensors to the internet. Engineering implementation of the system as well as its underlying ideology have been discussed. Micro Electro-Mechanical System (MEMS) sensors have low cost, smaller form factor as well as tend to work with a variety of IoT enabled systems. This work evaluates the feasibility of using them for condition monitoring in manufacturing. These systems embody a sort of primary unit for enabling the 'Smart Factory' revolution.

In this paper, we describe the design of a scientific workflow execution framework that integrates run-time verification to monitor its execution and checking it against the formal specifications. For controlling workflow execution, this framework provides for data provenance, execution tracking and online monitoring of each workflow task, also referred to as participants. The sequence of participants is described in an abstract parameterized view, which is used to generate concrete data dependency based sequence of participants with defined arguments. As participants belonging to a workflow are mapped onto machines and executed, periodic and on-demand monitoring of vital health parameters on allocated nodes is enabled according to prespecified invariant conditions with actions to be taken upon violation of invariants.

Large computing clusters used for scientific processing suffer from systemic failures when operated over long continuous periods for executing workflows. Diagnosing job problems and faults leading to eventual failures in this complex environment is difficult, specifically when the success of an entire workflow might be affected by a single job failure. In this paper, we introduce a model-based, hierarchical, reliable execution framework that encompass workflow specification, data provenance, execution tracking and online monitoring of each workflow task, also referred to as participants. The sequence of participants is described in an abstract parameterized view, which is translated into a concrete data dependency based sequence of participants with defined arguments. As participants belonging to a workflow are mapped onto machines and executed, periodic and on-demand monitoring of vital health parameters on allocated nodes is enabled according to pre-specified rules. These rules specify conditions that must be true pre-execution, during execution and post-execution. Monitoring information for each participant is propagated upwards through the reflex and healing architecture, which consists of a hierarchical network of decentralized fault management entities, called reflex engines. They are instantiated as state machines or timed automatons that change state and initiate reflexive mitigation action(s) upon occurrence of certain faults. We describe how this cluster reliability framework is combined with the workflow execution framework using formal rules and actions specified within a structure of first order predicate logic that enables a dynamic management design that reduces manual administrative workload, and increases cluster-productivity.

Virgo is an experiment aiming at the detection of gravitational waves emitted by astrophysical sources. Its detector, based on a 3km arms interferometer, is a complex setup which requires several digital control loops running up to 10kHz, an accurate and reliable central timing system and an efficient data acquisition, all of them being distributed over 3km. We overview here the main hardware and software components developed for the data acquisition system (DAQ) and its current architecture. Then, we briefly discuss its connections with interferometer's controls, especially through the automation of the interferometer's startup procedure. Then, we describe the tools used to monitor the DAQ and the performances we measured with them. Finally, are described also the tools developped for the online detector monitoring, mandatory complement of the DAQ for the commissioning of the Virgo detector.

Built at CERN by an international collaboration, the CLIC Test Facility 3 (CTF3) aims at demonstrating the feasibility of a high luminosity 3 TeV e + -e -collider by the year 2010. One of the main issues to be demonstrated is the generation of a high average current (30 A) high frequency (12 GHz) bunched beam by means of RF manipulation. At the same time, Free Electron Lasers (FEL) are developed in several places all over the world with the aim of providing high brilliance photon sources. These machines rely on the production of high peak current electron bunches. The required performances put high demands on the diagnostic equipment and innovative longitudinal monitors have been developed during the past years. This paper gives an overview of the longitudinal instrumentation developed at ELETTRA and CTF3, where a special effort was made in order to implement at the same time non-intercepting devices for online monitoring, and destructive diagnostics which have the advantage of providing more detailed information.

Current environmental challenges require sustainable and extended use and re-use of materials. For example, the service life of engineering tooling can be extended by using thin film coatings such as titanium nitride (TiN). However, when errors arise in the coating process or when the tooling needs to be re-used it is necessary to remove the coating. Decoating is also useful when a large batch of cutting tools needs to be re-directed for a different application, which requires a new generation of coating. Existing technology uses chemical methods which are not environmentally friendly or ideal for selective removal. In this work, excimer laser striping of TiN from coated tungsten carbide (WC) microtools has been demonstrated as a viable alternative to chemical methods. Also, in order to raise the integrity of the decoating process and to make the process more accurate and reliable, two online monitoring systems were developed exploiting probe beam reflection (PBR) and laser plume emission spectroscopy (PES). The online monitoring system facilitated a simultaneous prediction of surface elements as coating layers are progressively removed and ensures better control over the laser irradiation process so as to avoid under or over stripping of the coating.

The home automation or the smart home is a novel approach on smart living for smart people in Sri Lanka. The smart home concept is not very much popular in local customers as in other regions of the world. Modern homes provide electricity to electrical devices through the last element of the power supply chain, the power sockets and switches, which have been regarded traditionally as a mere junction. In fact, domestic power distribution system has not evolved as fast as other everyday devices, although they seem to be one of the best positioned candidates to be improved as smart homes are becoming increasingly popular. The paper addresses some of the most common problems that arise when interacting with the home automation and building management systems which show a novel approach to them: the prevention of unnecessary power consumption, wireless access and control of smart devices. The main tool for measuring system efficiency in homes and offices is the monitoring of the energy consumption of household appliances'. This paper describes the design and prototype implementation of a wireless home energy management system under a DC-distribution environment, which allows remote monitoring of appliances and energy monitoring. The proposed integrated prototype framework is embedded with three layers: ESP-12E IEEE 802.11 based communication layer for remote access, ESP-12E microcontroller as control layer to control the domestic appliances and DC input layer to input the DC power with voltage and current sensing. This system is suitable for maintaining a smart home which powered by a renewable energy such as solar or wind isolated from national grid on low voltage DC (LVDC) distribution systems. The research was tested for LVDC system in laboratory environment and need further modifications for low voltage AC (LVAC) distribution systems.

The license renewal and subsequent license renewal processes, defined in 10 CFR 54, provide a framework for U.S. nuclear plants to extend their operating licenses, but that process can ultimately result in costly, timely, resourceintensive efforts to satisfy the requirements for license extension. This report provides areas where the cost, time, and resource efforts may be reduced with additional analytical evaluation, model changes, inspection technique changes, and a review of past inspection results.

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers’ expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects’ attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import–export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accu...

Statistical process control charts operate with the two basic assumptions that the process data at different time points should be independent and identically normally distributed (i.i.n.d.). The assumption of independence is mostly violated which leads to wrong inferences made on the processes being monitored. This work aims at carrying out Statistical Process Control (SPC) of autocorrelated data exhibiting Geometric Brownian Motion (GBM): Arithmetic Return Model (ARM) approach. The proposed model proffers a simplified solution to the autocorrelation problem in SPC by transforming an Autoregressive of order 1 (AR (1)) process to ARM, this is because the GBM is the potential law which governs most positive time series, and has been confirmed to be AR(1) according to literature. Two sets of autocorrelated data which exhibit GBM properties; a simulated data and a furnace temperature data obtained from literature were subjected to SPC and monitoring. Findings from the work showed that the ARM performed better than the existing Logarithmic Return Model (LRM) in terms removal of autocorrelation faster with less sophistication, from 1.171261 to 2.86818 in the simulated data, and from 1.50446 to 1.7848 in the furnace temperature data. Also, the proposed model gave a better fitting of both process data, and faster detection ability for out of control signals in the control charts at λ=0.25, λ=0.5, λ=0.7 which makes the proposed ARM a better choice when dealing with SPC of autocorrelated data exhibiting the GBM.

As a recent trend, online monitoring techniques for electrical machines, mainly including power transformers, has been considered very important. This paper presents a new online method for detection of interturn faults in the power transformers using the ratio of negative sequence components of the primary and secondary line currents. The ratio is equal to the turn ratio during external faults as well as in the supply or load imbalance conditions, while it differs from the turn ratio when interturn winding faults occur. The main feature of the proposed method is its capability to detect low level interturn faults which typically cannot be detected by the traditional transformer protection devices before they developed into high level faults with more severe damage to the power transformer. In this work, a major improvement has been suggested to overcome this problem to a great extent. The proposed method can unambiguously detect interturn faults even down to two shorted turns along the winding. The method is not influenced by the supply and load harmonics even under heavily distorted conditions. The performance of the proposed technique was studied for a variety of operating conditions using data generated by simulations on a finite element model.

When Government drawings, specifications, or other data are used for any purpose other than in connection with a definitely related Government procurement operation, the United States Government thereby Incurs no responsibility nor any obligation whatsoever; and the drawings, specifications, or other data, is not to be regarded by implication or otherwise as in any manner licensing the holder or any other person or corporation, or conveying any rights or permission to manufacture use, or sell any patented invention that may in any way be related thereto. This report has been reviewed by the Office of Public Affairs (ASD/PA) and is releasable to the National Technical Information Service (NTIS). At NTIS, it will be available to the general public, including foreign nations. This technical report has been reviewed and is approved for publication.

When Government drawings, specifications, or other data are used for any purpose other than in connection with a definitely related Government procurement operation, the United States Government thereby Incurs no responsibility nor any obligation whatsoever; and the drawings, specifications, or other data, is not to be regarded by implication or otherwise as in any manner licensing the holder or any other person or corporation, or conveying any rights or permission to manufacture use, or sell any patented invention that may in any way be related thereto. This report has been reviewed by the Office of Public Affairs (ASD/PA) and is releasable to the National Technical Information Service (NTIS). At NTIS, it will be available to the general public, including foreign nations. This technical report has been reviewed and is approved for publication.

Several studies have been published considering fuel cell (FC) operation in normal conditions; but few of them addressed their operation under fault conditions. Among fault detection methods, a simple method based on the variable behaviour analysis is introduced. Basically, a fault supervisor is responsible for analysing unexpected changes in some variables, since each change in the variable behaviour can be attributed to a pre-classified fault cause, and then a fault diagnosis can be performed. The diagnosis considers several types of faults in polymer electrolyte membrane FC: faults in the reaction air feeding, rupture of the membrane-electrode assembly, low pressure in the hydrogen fuel line, and fault refrigeration system; those faults are detected based on the monitoring of a limited number of variables. Tests show that the methodology is reliable, flexible (once it permits the addition of other fault detection modules) and easy for implementation. C S average specific heat coefficient E Nernst open-circuit voltage I FC electrical current of the fuel cell M mass of the stack nr number of cells composing the stack P air air pressure P sat out saturated vapour pressure of the output air P w in partial pressure of water in the input air RH relative humidity T operating temperature T limit limit temperature V act voltage losses by activation of electrodes V ohmic voltage losses by resistance to protons and electrons flow V con voltage losses by mass concentration ΔQ ˙difference between generated and removed heat rate λ air stoichometry

The important issue of mechanical assemblies has been a subject of intense research over the past several years. Most electromechanical products are assemblies of several components, for various technical as well as economic reasons. This paper provides an object-oriented definition of an assembly model called the Open Assembly Model (OAM) and defines an extension to the NIST Core Product Model (NIST-CPM). The assembly model represents the function, form, and behavior of the assembly and defines both a system level conceptual model and associated hierarchical relationships. The model provides a way for tolerance representation and propagation, kinematics representation, and engineering analysis at the system level. The assembly model is open so as to enable plug-and-play with various applications, such as analysis (FEM, tolerance, assembly), process planning, and virtual assembly (using VR techniques). With the advent of the Internet more and more products are designed and manufactured globally in a distributed and collaborative environment. The class structure defined in OAM can be used by designers to collaborate in such an environment. The proposed model includes both assembly as a concept and assembly as a data structure. For the latter it uses STEP. The OAM together with CPM can be used to capture the assembly evolution from the conceptual to the detailed design stages. It is expected that the proposed OAM will enhance the assembly information content in the STEP standard. A case study example is discussed to explain the Usecase analysis of the assembly model.

The pressure tubes of pressurized heavy water reactor (PHWR) are affected by the operating conditions and they undergo changes in dimensions and material properties through irradiation damage & micro structural evolution. In order to assure the structural integrity of pressure tube during service, it is periodically examined by non-destructive testing techniques using BARC Coolant channel Inspection System (BARCIS). Pressure tube stresses are evaluated for both beginning and end of life for accounting the pressure tube dimensional changes that occur during its design life. At the beginning of life, the initial wall thickness and un-irradiated material properties are applied. At the end of life, pressure tube diameter and length increases, while wall thickness decreases. Full volumetric flaw examination scanning is carried out periodically to ensure the pressure tube physical integrity. Moreover, the rolled joint and adjacent area is highly prone to Delayed Hydride Cracking (DHC) due...

An algorithm is presented, that provides a fast and robust reconstruction of neutrino induced upward-going muons and a discrimination of these events from downward-going atmospheric muon background in data collected by the ANTARES neutrino telescope. The algorithm consists of a hit merging and hit selection procedure followed by fitting steps for a track hypothesis and a point-like light source. It is particularly well-suited for real time applications such as online monitoring and fast triggering of optical follow-up observations for multi-messenger studies. The performance of the algorithm is evaluated with Monte Carlo simulations and various distributions are compared with that obtained in ANTARES data.

The main problems in the practice of traditional shrimp aquaculture are related with maintaining good water quality and reducing high operational cost. In this paper it will be described the application of wireless sensors and Android based application as mobile monitoring tool in achieving highly efficient shrimp aquaculture monitoring system. A set of four water quality parameter sensors (pH, temperature, conductivity and DO) were submerged into the pond using a buoy, in which an electronics and Xbee wireless transmitter have been installed to transmit the measured data into a fixed monitoring station. The main component of the fixed monitoring station was a smart data logger capable of performing automatic aeration system. Data transmission from the monitoring station to the master station was done through GSM/GPRS module of a Raspberry microcontroller. Using internet connection, a web based server has been developed from which the Android based application retrieved the measured...

An investigation has been carried out into the deposits generated on cooled probes and the gas compositions produced from co-firing miscanthus mixed with Daw Mill coal. The biomass additions to the coal were at 0, 20, 40, 60, 80, 100 wt.% in a 100 kW th pilot-scale pulverised fuel (PF) combustion test rig operated with a feed rate of 7-15 kg/h. Online monitoring of the combustion gas stream was carried out for CO 2 , O 2 , H 2 O, SO 2 , CO, NO, NO 2 , N 2 O, HCl and HF using a high resolution multi-component Fourier Transform Infra-Red (FTIR) gas analyser. The deposits were collected from the upstream, side and downstream surfaces of three air-cooled probes that were exposed in the flue gas path and operated at surface temperatures of 500, 600 and 700 °C (to simulate heat exchanger tubes). The compositions of these deposits were determined using a range of analytical techniques including Environmental Scanning Electron Microscopy (ESEM) with Energy Dispersive X-ray (EDX) and X-ray Diffraction (XRD). The composition of the combustion gas streams and deposits changed as a function of the fuel mixtures used in the combustion process. The increasing miscanthus share in combination with the Daw Mill coal resulted in a reduction of SO x , NO x and HCl levels. The deposition on the upstream surfaces of the probes decreased with increasing biomass percentage, but increased on the downstream surfaces. The concentration of K and S in the deposits increased with increasing miscanthus share in the fuel up to 80 wt.%, particularly on the coolest probe (500 °C surface temperature). In addition, Cl was detected in the deposit on the two lower temperature probes only when 100% miscanthus was used in the combustion process. Thermodynamic modelling of the combustion/deposition process carried out in parallel also suggested formation of KCl only at the highest levels of biomass co-firing for miscanthus/Daw Mill coal, with K 2 SO 4 being predicted at lower co-firing levels for these specific fuels.

Non-destructive and destructive testing methods were employed to evaluate a documented boric acid leakage path through an Alloy 600 control rod drive mechanism (CRDM) penetration from the North Anna Unit 2 reactor pressure vessel head that was removed from service in 2002. A previous ultrasonic inservice-inspection (ISI) conducted by industry prior to the head removal, identified a probable leakage path in Nozzle 63 located in the interference fit between the penetration tube and the vessel head. In this current examination, Nozzle 63 was examined using phased array (PA) ultrasonic testing with a 5.0-MHz, eight-element annular array; immersion data were acquired from the nozzle inner diameter (ID) surface. A variety of focal laws were employed to evaluate the signal responses from the interference fit region. These responses were compared to responses obtained from a mockup specimen that was used to determine detection limits and characterization capabilities for wastage and boric acid presence in the interference fit region. Nozzle 63 was destructively examined after the completion of the ultrasonic nondestructive evaluation (NDE) to visually assess the leak paths. These destructive and nondestructive results compared favorably.

The still increasing high-level emissions of CO2 around the world is underlining the importance of wider adoption of renewable energy. With the advancement of new technology and development of organic solar cell, the price of photovoltaic is going down and encouraging large implementation of photovoltaic system. Due to the implementation of solar panels in locations that are often remote, it is very difficult to monitor physically. The traditional method of monitoring solar panels using PLC or SCADA systems is not cost effective. Therefore, with the development of advanced technology, we developed a data acquisition system using Internet of Things (IoT) technology. It is a new cost effective technology used to monitor organic solar cell using open source tools such as Integrated development environment and Thinkspeak. Thinkspeak is an IoT platform incorporated in Matlab that provides an environment to monitor solar panels parameters. This paper proposes a system for real-time data a...

The power generated in today’s time is dominated using fossil fuels that may get exhausted within the coming decades. Monitoring and management of the energy consumption hold a prime standing due to the discrepancy between power demand and production. Power meters are a part of every household, which provides us with a measurement of the overall power consumed in units. The problem with them is that they do not provide us with the individual consumption of an electrical appliance leaving customers unsatisfied with the electricity bills. This paper presents the design and application of a smart power management system (SPMS) using a variety of sensors combined with an Arduino microcontroller. This will be replacing conventional home sockets with smart sockets, allowing us to cover the monitoring, control and safety aspects of any individual appliance in the house. This system is based on a newly evolving field worldwide, called the Internet of Things (IoT). The SPMS will be measuri...

Pull through fundamental research into algorithms and techniques that can be translated and deployed. for all programme partners.  Demonstrate an intelligent system that reduces the data analysis burden currently experienced by asset analysts.  Additional case studies based on a broader asset base and driven by partner priorities.  Formalise software development plan.

Nuclear steam generators (NSGs) are key components of nuclear power plants (NPPs) since their reliability affects the overall plant performance. Corrosion control of steam generators under operation and during plant outages is attained by proper design, adequate material selection and control of water chemistry in the entire secondary cooling circuit. This article reviews the types of steam generators, the materials used for their construction (with a special focus on tubing alloys), the corrosion processes that may occur under operation and the strategies to preserve steam generators and other components of the secondary cooling system from corrosion during plant outages.

Hierarchical Agent Architecture for Scalable NoC Design with Online Monitoring Services Alexander Wei Yin, Liang Guang, Pasi Liljeberg, Pekka Rantala, Ethiopia Nigussie, Jouni Isoaho, Hannu Tenhunen Department of Information Technology, University of Turku, Finland {yinwei, ...

This paper presents a method based on neural networks to detect the broken rotor bars and end rings of squirrel cage induction motors. At first, detection methods are studied, and then traditional methods of fault detection and dynamic models of induction motors by using winding function model are introduced. In this method, all of the stator slots and rotor bars are considered, thus the performance of the motor in healthy situations or breakage in each part can be checked. The frequency spectrum of current signals is derived by using Fourier transformation and is analyzed in different conditions. In continuation, an analytical discussion and a simple algorithm are presented to detect the fault. This algorithm is based on neural networks. The neural network has been trained by using information of a 1.1 KW induction motor. This system has been tested with a different amount of load torque, and it is capable of working on-line and of recognizing all normal and ill conditions.

Global electricity demand has increased in the last few years. This need is growing all the time as energy consumption increases using conventional energy, which will soon be phased out. So, we had to look at alternative energies, namely renewable energies. The largest and most efficient of these is solar energy, and to make the most of this energy with the greatest efficiency, the performance of these solar panels needs to be directly monitored. This study presents an independent monitoring system based on the internet of things (IoT) to measure essential factors (terminal voltage, load current, energy consumption, humidity, temperature, and light intensity). These values are realistic and accurate, based on the sensors used to measure the aforementioned factors and then using the Node MCU ESP8266 to transmit the analyzed data to the circuit. The Thingspeak platform was then employed to display, analyze, and store these results in real time.

An advanced non-invasive, field-suitable and inexpensive leaf patch clamp pressure probe for online-monitoring of the water relations of intact leaves is described. The probe measures the attenuated output patch clamp pressure, P p , of a clamped leaf in response to an externally applied input pressure, P clamp . P clamp is generated magnetically. P p is sensed by a pressure sensor integrated into the magnetic clamp. The magnitude of P p depends on the transfer function, T f , of the leaf cells. T f consists of a turgor pressureindependent (related to the compression of the cuticle, cell walls and other structural elements) and a turgor pressure-dependent term. T f is dimensionless and assumes values between 0 and 1. Theory shows that T f is a power function of cell turgor pressure P c . Concomitant P p and P c measurements on grapevines confirmed the relationship between T f and P c . P p peaked if P c approached zero and assumed low values if P c reached maximum values. The novel probe was successfully tested on leaves of irrigated and nonirrigated grapevines under field conditions. Data show that slight changes in the microclimate and ⁄ or water supply (by irrigation or rain) are reflected very sensitively in P p .