On-line Data Acquisition System Research Articles

Experimental performance analysis for reverse osmosis pilot plant subjected to different brackish salinity spectrum

The main obstacles to reverse osmosis desalination processes are high energy intensity and long-term continuity. Temperature and pressure have the greatest and most significant effects on energy use. The main objectives of the current study are to determine the pressure and temperature-dependent optimal operating parameters for a membrane desalination unit. To determine the ideal operating settings for a membrane unit, the impact of changing pressure and temperature on its performance was investigated. These two elements are closely connected to the energy consumption per cubic meter under various operating situations. The present work is experimentally carried out in a research laboratory for capacity building and future research studies in the desalination field established in the National Center of Water Research – Egypt. This laboratory is Egypt’s first multi-functional Desalination Research Station for seawater, brackish water, and related water treatment areas. The plant is equipped with online instrumentation and Data Acquisition System and 13 sensors for most physical parameters which economically affect membrane performance and desalination processes. These parameters, particularly pressure and temperature, are measured, evaluated and analyzed. According to the findings in this study, feed salinity and feed pressure both have significant impacts.At 13 bar pressure, the maximum salt rejection was 98.8%. When feed pressure is increased from 5 to 13 bars, there is a 73.3% decrease in permeate salinity. Additionally, applying a 13 bar feed pressure to water with a salinity of 1000 ppm results in the best water quality of 12 ppm. The relationship between feed pressure, brine salinity, and membrane water recovery appeared to be approximately linear and positive. More crucially, it was discovered that feed pressure, salinity, and water recovery are all constants for water permeability. A prototype for the maximum pressure (ranges from 15.6 to 10.8) and temperature (ranges from 21 to 35) at which the optimal recovery of the laboratory occurred was developed. Moreover, the developed prototype includes the corresponding permeate TDS and a specific energy for each optimal point.

Status of the data acquisition, trigger, and slow control systems of the Mu2e experiment at Fermilab

The Mu2e experiment at the Fermilab will search for a coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10,000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq framework as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates several framework components — an artdaq-based DAQ, an art-based event processing, and an EPICS-based detector control system (DCS), and provides a uniform multi-user interface to its components through a web browser. Data streams from the Mu2e tracker and calorimeter are handled by the artdaq-based DAQ and processed by a one-level software trigger implemented within the art framework. Events accepted by the trigger have their data combined, post-trigger, with the separately read out data from the Mu2e Cosmic Ray Veto system. Foundation of the Mu2e DCS, EPICS – an Experimental Physics and Industrial Control System – is an open-source platform for monitoring, controlling, alarming, and archiving. A prototype of the TDAQ and the DCS systems has been built and tested over the last three years at Fermilab’s Feynman Computing Center, and now the production system installation is underway. This work presents their status and focus on the installation plans and procedures for racks, workstations, network switches, gateway computers, DAQ hardware, slow controls implementation, and testing.

A Low-Cost Online Data Acquisition and Processing System for Industrial Pollutants’ Monitoring

Recently, the prevention and control of industrial pollution has attracted extensive attention. Many studies based on emission data have achieved great success, but there are relatively few studies on the emission data acquisition. To this end, we propose a data acquisition and processing system (DAPS) for online monitoring of industrial pollutants. The system receives real-time emission data through the RS485, automatically generates periodic data, and saves it in the SQL Server database. Real-time Ethernet (RTE) is used to transmit control signals between the system and programmable logic controllers (PLCs). Emission data is transmitted to local industrial control systems and remote monitoring centers (RMCs) via a wireless network. We tested the acquisition function and communication function of the system through experiments and evaluated the overall performance of the system by observing the memory and CPU usage. The system is characterized by simple implementation and versatility and can be applied to continuous monitoring of various industrial pollutants.

Mu2e DAQ and slow control systems

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10,000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multiuser, web-based, interface accessible through a web browser. The data stream from the detector subsystems is read by a software filter algorithm that selects events which are combined with the data flux coming from a Cosmic Ray Veto System. The Detector Control System (DCS) has been developed using the Experimental Physics and Industrial Control System (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS System has been integrated into otsdaq. A prototype of the TDAQ and the DCS systems has been built at Fermilab’s Feynman Computing Center. In this paper, we report on the progress of the integration of this prototype in the online otsdaq software.

Data acquisition and slow control interface for the Mu2e experiment

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10000 over existing limits. The Mu2e Trigger and Data Acquisition System (TDAQ) uses otsdaq as the online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The data stream from the detector subsystems is read by a software filter algorithm that selects events which are combined with the data flux coming from a cosmic ray veto system. The Detector Control System (DCS) has been developed using the Experimental Physics and Industrial Control System (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS system has been integrated into otsdaq. A prototype of the TDAQ and the DCS systems has been built at Fermilab’s Feynman Computing Center. In this paper, we report on the progress of the integration of this prototype in the online otsdaq software.

Prototype Data Acquisition and Slow Control Systems for the Mu2e Experiment

The Mu2e experiment at the Fermilab Muon Campus will search for the coherent neutrinoless conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement by a factor of 10 000 over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable with direct searches at either present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system exploits otsdaq as its online Data Acquisition System (DAQ) solution. Developed at Fermilab, otsdaq integrates both the artdaq DAQ and the art analysis frameworks for event transfer, filtering, and processing. otsdaq is an online DAQ software suite with a focus on flexibility and scalability and provides a multi-user, web-based, interface accessible through a web browser. The read out controllers (ROCs) stream out zero-suppressed data continuously from the detector subsystems to the data transfer controllers (DTCs). The data stream is then read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events which are combined with the data flux coming from a cosmic-ray veto (CRV) system. The detector control system (DCS) has been developed using the experimental physics and industrial control system (EPICS) open source platform for monitoring, controlling, alarming, and archiving. The DCS has been integrated into otsdaq. A prototype of the TDAQ system and the DCS has been built at Fermilab's Feynman Computing Center. In this article, we report on the progress of the integration of this prototype in the online otsdaq software.

Lean meat yield estimation using a prototype 3D imaging approach

Lean Meat Yield (LMY, %) of carcass is an important industry trait, which currently is not routinely measured in Australian beef abattoirs. Objective on-line technology to determine LMY is key for wider adoption. This paper presents a proof-of-concept approach for estimating the LMY of beef carcasses from the 3D information provided by RGB-D cameras. Moreover, a specifically designed on-line data acquisition system for abattoir applications is presented, consisting of three cameras moving on a scanning rig to generate 3D carcass side reconstructions. The hindquarter is then segmented consistently across all the 3D models to extract curvature information and LMY estimated via non-linear regression based on Gaussian Process models. Sides from 119 carcasses at two different commercial abattoirs were used to evaluate this approach. Results from this preliminary study (RMSE = 3.91%, R2 = 0.69) using curvature, P8 fat and HSCW indicate that 3D imaging of beef carcasses is a viable and relatively accurate technology to estimate LMY.

Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions

Given the advances in online data acquisition systems, statistical learning models are increasingly used to forecast wind speed. In electricity markets, wind farm production forecasts are needed for the day-ahead, intra-day, and real-time markets. In this work, we use a spatiotemporal model that leverages wind dynamics to forecast wind speed. Using a priori knowledge of the wind direction, we propose a maximum likelihood estimate of the inverse covariance matrix regularized with a hierarchical sparsity-inducing penalty. The resulting inverse covariance estimate not only exhibits the benefits of a sparse estimator, but also enables meaningful sparse structures by considering wind direction. A proximal method is used to solve the underlying optimization problem. The proposed methodology is used to forecast six-hour-ahead wind speeds in 20-minute time intervals for a case study in Texas. We compare our method with a number of other statistical methods. Prediction performance measures and the Diebold–Mariano test show the potential of the proposed method, specifically when reasonably accurate estimates of the wind directions are available.

FPGA accelerated computing for particle identification in high-energy physics experiments

The key challenges in the recent nuclear and High-Energy Physics (HEP) experiments are high reaction rate and data rate. The data rate, the product of the event size and the reaction rate, ranges from 107 up to more than 1011 Bytes/s. This huge amount of data cannot be entirely recorded and processed during offline analysis. Interestingly, physicists have interest only in a very small proportion of all the event data that occur only once within one million interactions. Hence it provides the possibility to utilize an efficient online Data Acquisition (DAQ) and Trigger system to reject uninteresting events while identify and retain interesting ones in real-time. This work assesses the machine learning assisted particle identification algorithms on Field-Programmable-Gate-Array (FPGA) based computing platform for HEP experiments. To achieve the highest classification accuracy in particle identification the combination of particle recognition algorithms and random decision forests algorithms are laid on an accelerated computing platform utilizing the FPGA based computing platform to classify the particles of interest. The simulation results of this implementation achieve many benefits through dynamic and intelligent design management, such as efficient resource utilization and high performance-cost ratio.

Architecture of on-line data acquisition system for car on-board diagnostics

Modern cars produced for the last two decades are full of electronic devices called Electronic Control Units (ECU). They are responsible for collecting diagnostic data from different components such as the engine, breaks etc. using probes and sensors. The collected data are validated against built-in heuristic and abnormal behaviour is reported to a driver by a gauge on an instrument cluster. ECUs use data provided by other ECUs. Information is transmitted over the dedicated network called Controlled Area Network (CAN). Every car equipped with ECUs and CAN exposes information over universal diagnostic interface called On-Board Diagnostic. Using the interface, it is possible to gather car's live data. With the data mining approach, it is possible to exploit the collected more effectively to obtain much more information about the functioning of car components than it is provided by standard vehicle equipment. The paper describes how to build a laboratory set to facilitate automated data collection. It consists of three major components: data acquisition, automated logs collection and persistent storage with presentation tools. The first component is based on Torque application for which reverse engineering was performed.

Improved Detection of Supernovae with the IceCube Observatory

The IceCube neutrino telescope monitors one cubic kilometer of deep Antarctic ice by detecting Cherenkov photons emitted from charged secondaries produced when neutrinos interact in the ice. The geometry of the detector, which comprises a lattice of 5160 photomultipliers, is optimized for the detection of neutrinos above 100 GeV. However, at subfreezing ice temperatures, dark noise rates are low enough that a high flux of MeV neutrinos streaming through the detector may be recognized by a collective rate enhancement in all photomultipliers. This method can be used to search for the signal of core collapse supernovae, providing sensitivity competitive to Mton neutrino detectors to a supernova in our Galaxy. An online data acquisition system dedicated to supernova detection has been running for several years, but its shortcomings include limited sampling frequency and the fact that the burst energy and direction cannot be reconstructed. A recently developed offline data acquisition system allows IceCube to buffer all registered photons in the detector in case of an alert with low probability to be erroneous. By analyzing such data offline, a precision determination of the burst onset time and the characteristics of rapidly varying fluxes, as well as estimates of the average neutrino energies may be obtained. For supernovae ending in a black hole, the IceCube data can also be used to determine the direction of the burst.

A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data

Abstract Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium-term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center.

Design and implementation of new control room system in Damavand tokamak

The aim of this paper is design and implementation of an up-to-date control room. The previous control room had a lot of constraints and it was not apposite to the sophisticated diagnostic systems as well as to the modern control and multivariable systems. Although it provided the best output for the considered experiments and implementing offline algorithms among all similar plants, it needed to be developed to provide more capability for complex algorithm mechanisms and this work introduces our efforts in this area. Accordingly, four leading systems were designed and implemented, including real-time control system, online Data Acquisition System (DAS), offline DAS, monitoring and data transmission system. In the control system, three real-time control modules were established based on Digital Signal Processor (DSP). Thanks to them, implementation of the classic and linear and nonlinear intelligent controllers was possible to control the plasma position and its elongation. Also, online DAS was constructed in two modules. Using them, voltages and currents of charge for the capacitor banks and pressure of different parts in vacuum vessel were measured and monitored. Likewise, by real-time processing of the online data, the safety protocol of plant performance was accomplished. In addition, the offline DAS was organized in 13 modules based on Field Programmable Gate Array (FPGA). This system can be used for gathering all diagnostic, control, and performance data in 156 channels. Data transmission system and storing mechanism in the server was provided by data transmitting network and MDSplus standard protocol. Moreover, monitoring software was designed so that it could display the required plots for physical analyses. Taking everything into account, this new platform can improve the quality and quantity of research activities in plasma physics for Damavand tokamak.

A real-time online data acquisition system for Dragon-I linear induction accelerator

An extensible high-speed accelerator data acquisition system (ADAS) for the Dragon-I linear induction accelerator has been developed. It comprises a VXI crate, a controller, four data acquisition plug-ins, and a host computer. A digital compensation algorithm is used to compensate for the distortion of high-speed signals arising from long-distance transmission. Compared with the traditional oscilloscope wall, ADAS has significant improvements in system integration, automation, and reliability. It achieves unified management of data acquisition and waveform monitoring and performs excellently with a 107-ps high-accuracy trigger and 32-channel signal monitoring. In this paper, we focus on the system architecture and hardware design of the ADAS, realization of the trigger, and digital compensation algorithm.

Arduino-based noise robust online heart-rate detection

This paper introduces a noise robust real time heart rate detection system from electrocardiogram (ECG) data. An online data acquisition system is developed to collect ECG signals from human subjects. Heart rate is detected using window-based autocorrelation peak localisation technique. A low-cost Arduino UNO board is used to implement the complete automated process. The performance of the system is compared with PC-based heart rate detection technique. Accuracy of the system is validated through simulated noisy ECG data with various levels of signal to noise ratio (SNR). The mean percentage error of detected heart rate is found to be 0.72% for the noisy database with five different noise levels.

The electronics, online trigger system and data acquisition system of the J-PARC E16 experiment

The J-PARC E16 experiment was proposed to investigate the restoration of chiral symmetry at the normal nuclear density. E16 will systematically measure in-medium mass of vector mesons at J-PARC Hadron Experimental Facility using a 30-GeV proton beam with an intensity of 2 x 1010 protons per pulse. The E16 spectrometer was designed to detect e+e- from slowly moving vector meson, particularly the ϕ meson. The detector system consists of GEM tracker, Gas Cerenkov detector based on GEM and electromagnetic calorimeter made of lead-glass, whose number of channels reaches about 100,000 in total. The readout electronics, trigger system and data acquisition system for the detectors have been developed, for which level-1 trigger rate of 1-2 kHz is required under several 10 MHz interaction rate. The preparation is underway for the first beam time in 2017.

The Trigger and Data Acquisition System for the 8 tower subsystem of the KM3NeT detector

KM3NeT is a deep-sea research infrastructure being constructed in the Mediterranean Sea. It will host a large Cherenkov neutrino telescope that will collect photons emitted along the path of the charged particles produced in neutrino interactions in the vicinity of the detector. The philosophy of the DAQ system of the detector foresees that all data are sent to shore after a proper sampling of the photomultiplier signals. No off-shore hardware trigger is implemented and a software selection of the data is performed with an on-line Trigger and Data Acquisition System (TriDAS) to reduce the large throughput due to the environmental light background. A first version of the TriDAS has been developed to operate a prototype detection unit deployed in March 2013 in the abyssal site of Capo Passero (Sicily, Italy), about 3500m deep. A revised and improved version has been developed to meet the requirements of the final detector, using new tools and modern design solutions. First installation and scalability tests have been performed at the Bologna Common Infrastructure and results comparable to what expected have been observed.

An Experimental Study of the Flow Field Inside the Diffuser Passage of a Laboratory Centrifugal Pump

Measurements are processed on a centrifugal pump model, which works with air and performs with the vane-island type diffuser of a real hydraulic pump, under five flow rates to investigate the internal flow characteristics and their influence on overall pump performance. The mean flow characteristics inside the diffuser are determined by using a miniature three-hole probe connected to an online data acquisition system. The flow structure at the inlet section of the diffuser is analyzed in detail, with a focus on the local pressure loss inside the vaneless gap and incidence angle distributions along the hub-to-shroud direction of the diffuser. Some existing calculations, including leakage effects, are used to evaluate the pressure recovery downstream of the impeller. Furthermore, particle image velocimetry (PIV) measurement results are obtained to help analyze the flow characteristics inside the vane-island diffuser. Each PIV measuring plane is related to one particular diffuser blade-to-blade channel and is analyzed by using the time-averaged method according to seven different relative positions of the impeller. Measurement results show that main loss is produced inside the vaneless part of the diffuser at low flow rates, which might have been caused by the strong rotor–stator interaction. When the impeller flow rate is greater than the diffuser design flow rate, a large fluctuating separated region occurs after the throat of the diffuser on the pressure side. Mean loss originates from the unsteady pressure downstream of the diffuser throat. For better characterization of the separations observed in previous experimental studies, complementary unsteady static pressure measurement campaigns have been conducted on the diffuser blade wall. The unsteadiness revealed by these measurements, as well as theirs effects on the diffuser performance, was then studied.

English

This paper is about the application of data acquisition systems in industrial requirements for real time execution of events with industrial process control and automation. The main core of the system is an embedded hardware running a scaled-down version of Linux: a popular choice of operating system for embedded applications. On -line embedded web server is a challenging part of many embedded and real time data acquisition and control system applications. The World Wide Web is a global system of interconnected computer networks that use the standard Internet Protocol Suite (TCP/IP) to serve billion of users worldwide and allows the user to interface many real time embedded applications like data acquisition, Industrial automations and safety measures etc., In this project we approached towards the design and development of on-line Interactive Data Acquisition and Control System (IDACS) using ARM based embedded web server. It can be a network, intelligent and digital distributed control system. Single chip IDACS method improves the processing capability of a system and overcomes the problem of poor real time and reliability. This system uses ARM9 Processor portability with Linux operating system it makes the system which handling various processes based on multi tasking and reliable scheduling mechanisms. A novel approach is introduced to minimize the operational costs while operating with a large amount of data. The system is demonstrated to be suitable for different embedded applications by attaching several real-time modules through appropriate interfaces. The embedded web server technology is the combination of embedded device and Internet technology, through this embedded web server user can access their equipments remotely. The equipment mentioned here could be home appliances and factory devices. This paper is focused on realization of TCP/IP suite and user development platform for this embedded web server. The embedded web server design includes a complete web server with TCP/IP support and Ethernet interface. Web pages are written by Hyper text markup language (HTML).

Damage detection with streamlined structural health monitoring data.

The huge amounts of sensor data generated by large scale sensor networks in on-line structural health monitoring (SHM) systems often overwhelms the systems’ capacity for data transmission and analysis. This paper presents a new concept for an integrated SHM system in which a streamlined data flow is used as a unifying thread to integrate the individual components of on-line SHM systems. Such an integrated SHM system has a few desirable functionalities including embedded sensor data compression, interactive sensor data retrieval, and structural knowledge discovery, which aim to enhance the reliability, efficiency, and robustness of on-line SHM systems. Adoption of this new concept will enable the design of an on-line SHM system with more uniform data generation and data handling capacity for its subsystems. To examine this concept in the context of vibration-based SHM systems, real sensor data from an on-line SHM system comprising a scaled steel bridge structure and an on-line data acquisition system with remote data access was used in this study. Vibration test results clearly demonstrated the prominent performance characteristics of the proposed integrated SHM system including rapid data access, interactive data retrieval and knowledge discovery of structural conditions on a global level.