LabVIEW Virtual Instrument Research Articles

Implementation of LabVIEW as a Virtual Instrument in a Cost‐Effective Isolated Lung Setup

IntroductionIsolated lung experiments allow for ex vivo characterization of lung function and prove to be a valuable model for various fields such as cancer, lung transplantation, and ischemia/reperfusion injury. Unlike other models that require damaging the lung, the isolated lung allows for collection of physiological changes including airway pressure, pulmonary artery and vein pressure, airway flow, and, in our model, ventilated gas concentrations. We describe the use of LabVIEW combined with the free statistical software R for highly flexible, effective, and automated data collection in an isolated lung system.MethodsPressure transducers connected to bioamplifiers allow for collection of dynamic pulmonary artery and vein pressures, while airway pressure and flow are collected from the ventilator with the former used to visualize positive end expiratory pressure. Signals from these devices are connected to an analog‐digital converter from National InstrumentsTM that collects data into LabVIEW software, which is then automatically collected and displayed at 200Hz. Use of a roller pump with analog input/output allows for fully automated control of the perfusion speed. Output from a gas mixer for use in ventilated gas studies is collected and used to generate gas composition data. Additional experimental data that do not have signal output capability can be recorded manually in LabVIEW.ResultsLabVIEW is a reliable alternative to other commercially available data acquisition systems. The creation of a virtual instrument requires significant programming knowledge but allows for completely customizable data collection in a modular isolated lung system. Data become available in an Excel‐compatible file which can then be imported and analysed in R as another affordable alternative to commercial programs.ConclusionWe describe the design and implementation of a LabVIEW virtual instrument as an adjustable alternative to conventional data acquisition systems.Support or Funding InformationThis work was supported, in part, by institutional funds, NIH grant (5R01 HL123227), and a Merit Review Award (I01 BX003482) from the U.S. Department of Veterans Affairs Biomedical Laboratory R&D Service.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Design and Implementation of Education and Training Graphical User Interface (GUI) Based on NI LabVIEW for the FESTO MPS PA Compact Workstation

This paper aims to tackle the limitation of having predefined controllers, On-Off, Proportional (P), Proportional Integral (PI), Integral (I), Proportional Integral Derivative (PID), to control the FESTO MPS PA compact workstation using the Fluid Lab software. The current software allows users to change the parameters of the selected control structure; however, it is not possible to change the controller structure and/or to implement different control strategies. In this paper, several LabVIEW Virtual Instruments (VIs) are created to manipulate actuators and obtain information from the sensors of the MPS PA compact workstation. The proposed software interface will be used for training students at Abu Dhabi Polytechnic on topics related to process control design and implementation. To demonstrate the capabilities of the proposed LabVIEW software interface, different control strategies including fuzzy PID are implemented for the four control loops that are supported by the MPS workstation. Experimental results for the level and flow rate control loops using PID and fuzzy controllers are provided as examples.

Laser spot image acquisition and processing based on LabVIEW

With the wide application of laser technology in various fields, how to quickly and quantitatively evaluate the characteristic parameters of laser spot becomes a hot issue in laser technology research. In this paper, based on the virtual instrument LabVIEW platform, the image is captured by USB camera, the NI-IMAQdx module is used for programming.Through image acquisition, gradation transformation, filtering processing, threshold segmentation and edge detection, the pseudo-color transformation display, centroid, long and short axis values and ellipticity of laser spot intensity are obtained, the corresponding display level is 0–256, and the working wavelength range is 400 nm–1100 nm, which satisfies the testing and analysis requirements in most current environments.

The Research and Practice of a Long Pulse Synchronous Data Measurement Method

The research and implementation of a long pulse synchronous data measurement method is mainly presented in this paper from the aspects of requirement analysis, hardware platform support, software design and testing result. The NI multi-function data acquisition board card is adopted as the hardware platform. The software program platform is based on the LabVIEW virtual instrument platform. The long-pulse data acquisition technology, LabVIEW multithread data processing technology, computer transmission control technology and data compression processing technology as the key technologies are applied in this paper. The main functions of the system include (1) accurate sampling for the long pulse measured signal at the specified sampling frequency and sampling time; (2) a variety of acquisition control modes are provided to meet the different application requirements; (3) the data collected is easily stored and retrieved after a specified algorithm; (4) the real-time display and historical playback functions for measuring signals; (5) the data transmission and storage functions; (6) simple and friendly interface which is easy to operate. By software testing, the software can meet the functional requirements above and it can meet the data sampling at 1000s or even longer with 50KHz sampling rate.Experiments show that it can work stably and reliably, and it has a very important practical application value. This software has universality and can provide valuable reference for the applications of long pulse data measurement based on other hardware systems.

Data-driven soft sensor for animal cell suspension culture process based on DRVM

In order to solve the problem that key state variables (such as glucose concentration, lactic acid concentration and cell density) in the dynamic process of animal cell suspension culture are difficult to be measured in real time, a data-driven soft sensor based on dynamic relevance vector machine (DRVM) algorithm is proposed. The dominant variables of the soft sensor model are selected according to the mechanisms process. The corrcoef( ) function (belongs to the correlation coefficient command in MATLAB) is used to analyze the correlation among environmental variables, and the auxiliary variables of the soft sensor model are further determined. An improved method on the three edge location algorithm is used to optimize the dynamic weights of the DRVM model. Considering the influence of dynamic transition on soft sensor, the maximum likelihood distribution method under the Bayesian framework is used to train DRVM weight and super parameters, and the dynamic soft sensor model of animal cell suspension culture is established. The proposed method is applied to predict the key state variables in BHK-21 cell suspension culture Process. The experimental results show that compared with the traditional static soft sensing based on RVM, the data-driven soft sensor based on DRVM has higher accuracy, and the rationality and superiority of the method are verified. In order to further realize the real-time online prediction of key state variables, the monitoring interface of the suspension culture process is developed on the LabVIEW virtual instrument platform through its MATLAB Script node, and the data exchange of the DRVM soft sensor for the key state variables of the cell suspension culture process based on MATLAB and monitoring interface is realized.

Implementation of a Distributed Optimal Predictive Control in a Quadruple Tank System

This paper presents the development of a distributed optimal model predictive control (DOMPC) applied to a quadruple tank system. The DOMPC is based on an optimal model predictive control (OMPC) approach. In order to distribute the control laws, the DOMPC divide the controller in s local controllers. The local controllers are communicated by a local area network (LAN). For the control input calculation, it is assumed that this LAN introduces a delay of one sample. A Labview virtual instrument, that allows to implement and configure the DOMPC controller, is developed in order to apply the controller in a quadruple tank process. This virtual instrument uses an intuitive graphical interface to adjust the controller parameters and incorporates a simple but effective strategy to cope with information losses in the LAN. The development is a distributed scheme that has the benefit of coordinate the tasks from local controllers to achieve the global control objective. Finally, the results obtained are compared with a centralized OMPC in order to evaluate its performance and its applicability.

Fault-Tolerant Control for ROVs Using Control Reallocation and Power Isolation

This paper describes a novel thruster fault-tolerant control system (FTC) for open-frame remotely operated vehicles (ROVs). The proposed FTC consists of two subsystems: a model-free thruster fault detection and isolation subsystem (FDI) and a fault accommodation subsystem (FA). The FDI subsystem employs fault detection units (FDUs), associated with each thruster, to monitor their state. The robust, reliable and adaptive FDUs use a model-free pattern recognition neural network (PRNN) to detect internal and external faulty states of the thrusters in real time. The FA subsystem combines information provided by the FDI subsystem with predefined, user-configurable actions to accommodate partial and total faults and to perform an appropriate control reallocation. Software-level actions include penalisation of faulty thrusters in solution of control allocation problem and reallocation of control energy among the operable thrusters. Hardware-level actions include power isolation of faulty thrusters (total faults only) such that the entire ROV power system is not compromised. The proposed FTC system is implemented as a LabVIEW virtual instrument (VI) and evaluated in virtual (simulated) and real-world environments. The proposed FTC module can be used for open frame ROVs with up to 12 thrusters: eight horizontal thrusters configured in two horizontal layers of four thrusters each, and four vertical thrusters configured in one vertical layer. Results from both environments show that the ROV control system, enhanced with the FDI and FA subsystems, is capable of maintaining full 6 DOF control of the ROV in the presence of up to 6 simultaneous total faults in the thrusters. With the FDI and FA subsystems in place the control energy distribution of the healthy thrusters is optimised so that the ROV can still operate in difficult conditions under fault scenarios.

Metrological characterization of a combined bio-impedance plethysmograph and spectrometer

In this paper, the design and characterization of a device, able to analyze the electrical bio-impedance in the frequency domain and its variations with time, is presented. The whole system design, that employs a tetra-polar electrode configuration, a specifically developed printed circuit board, and a data acquisition card connected to a PC, is presented. The measurement system is controlled through LabVIEW virtual instruments. Different reference resistors and RC networks, with impedance values lying in the physiological range of bio-impedances, have been used for assessing the systematic and random uncertainty contributions, and to derive the calibration curves necessary to correct the systematic effects of the developed instrument. The obtained results show that it is possible to achieve excellent metrological performances with the proposed system, that is suitable to be developed in a low-cost and portable version. Two possible applications of the instrument are shown, i.e. cardio-respiratory activity monitoring and bioelectrical impedance spectroscopy.

Evaluation of natural frequencies and damping ratios of coconut shell particles reinforced epoxy composites

Modal analysis comprises of determination of natural frequency, mode shapes and damping factor of dynamic structures. It is essential in analyzing the response of a structure in real-life situations to avoid resonance condition under excitation. Now a days, composites with natural fiber/particle materials are in considerable demand for structural applications as an alternative material to conventional synthetic fiber composites. However, adequate amount of work on modal analysis of these composites has not been noticed in the literature. The objective of the present work is to evaluate the influence of coconut shell particle size and its volume fraction on natural frequencies and damping factor of coconut shell particles filled epoxy composites. Coconut shell particles reinforced composites (CSPE) were developed using open mould technique with particle sizes of 0.25, 0.5,1 and 2 mm and particle volume fractions of 40%, 50% and 60%. The experimental set up consists of a composite cantilever beam (270 mm × 40 mm × 12 mm), impact hammer, accelerometer and computer interfaced data acquisition system. Acceleration v/s Time plots were acquired using LAB VIEW virtual instrumentation software and converted into Amplitude v/s Frequency plot by Fast Fourier Transform (FFT) using MAT LAB. The natural frequencies are also predicted theoretically and numerically (using ANSYS). The damping ratio was determined using half power band width method. The results reveal that as the particle size and its volume fraction increases, the natural frequency decreases whereas it is vice versa in case of damping factor. The experimental, theoretical and numerical values of natural frequencies are found to be in good agreement. It is concluded that CSPE composites possesses the damping ratios comparable to conventional composite materials such as glass fiber filled epoxy composites.

Labview Virtual Instrument Based on Intelligent Management and Monitoring of Microclimate in Precision Pig Farming with Wireless Sensor Network

Labview Virtual Instrument Based on Intelligent Management and Monitoring of Microclimate in Precision Pig Farming with Wireless Sensor Network

Multi‐layer photovoltaic fault detection algorithm

This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system. For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software. Furthermore, a third-order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios. The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function. The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system. The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%; however, the fault DA is increased up to a minimum value of 98.8% after considering the fuzzy logic system.

Diseño de Herramientas Didácticas Enfocadas al Aprendizaje de Sistemas de Control Utilizando Instrumentación Virtual

This paper describes the design of three didactic tools focused on learning of control systems implemented in LabVIEW virtual instruments software. These tools are dedicated to stability analysis in control systems, compensator design using root locus approach and Bode diagrams in the frequency domain. Each of them has a friendly interface with the user. The advantage of these didactic tools is the several options to simulate some characteristics referent to control in contrast with others teaching tools.

Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system

This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behavior of an existing PV system. For a given set of working conditions, solar irradiance and PV modules' temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits for the VR and PR ratios obtained using VI LabVIEW simulation tool.The high and low detection limits are compared with measured data taken from 1.1 kWp PV system installed at the University of Huddersfield, United Kingdom. Samples lie out of the detection limits are processed by a fuzzy logic classification system which consists of two inputs and one output membership function.In this paper, PV faults corresponds to a short circuited PV module. The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system, where the maximum detection accuracy of before considering the fuzzy logic system is equal to 95.27%. However, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy system.

Simultaneous fault detection algorithm for grid‐connected photovoltaic plants

In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK.

Online Dynamic Balance Detection Method of High Speed Motorized Spindle Based on LabVIEW

Aiming at the dynamic unbalance problem of high speed motorized spindle, an online dynamic balance detection method based on Fourier transform, polynomial curve fitting and Hooke's law is proposed, at the same time, based on virtual instrument LabVIEW software platform, programming deal with the vibration spectrum image of the motorized spindle. Combined with the detection method, output signal of the size, displacement and vector direction of unbalance force of the motorized spindle are obtained, the output signal would be transmit to dynamic balancing device to complete regulation of high speed motorized spindle online dynamic balance and achieve the real-time monitoring, analysis and timely adjustment of dynamic balance of high speed motorized spindle by using the programming of virtual instrument. We verified detection method by the experiment of online dynamic balance adjustment of the motorized spindle. Detection method of dynamic balance of motorized spindle based on LabVIEW can intuitively test vibration characteristics, the accurate detection of online dynamic balance is helpful to improve the rotation precision of the high speed spindle and realize high quality parts processing.Key words: Motorized spindle; the dynamic balance; Online detection; LabVIEW

Design of Communication Function Test System for Intelligent Low Voltage Electrical Apparatus Based on LabVIEW and Modbus RTU Protocol

On the brief introduction of the characteristics of Modbus RTU communication protocol commonly used in intelligent low voltage electrical apparatus and analyze test requirements of intelligent low voltage electrical apparatus communication function, a design of communication function test system based on LabVIEW and Modbus RTU protocol for intelligent low voltage electrical apparatus is introduced. The system hardware is made up of industrial computer and RS232 / RS485 converter. Introduces in detail the The design and realization of read, write and CRC check software for Modbus RTU protocol communication based on LabVIEW virtual instrument technology development platform is discussed in detail. Practical application results shows that the test system could conduct a comprehensive, safe, reliable detection for intelligent low voltage apparatus communication function, have the advantages of strong anti-interference ability and low cost.

LabVIEW-based Classic, Fuzzy and Neural Controllers Algorithm Design Applied to a Level Control Prototype

This work presents the algorithm design of classic control, fuzzy control and neural control applied to a level control prototype. Classic control systems use PI and PID control actions; in order to tune such actions, Ziegler and Nichols oscillating method was used. It was designed a proportional, derivative and incremental fuzzy control system. As well, an inverse mode neural controller and an intern model neural controller are presented. As a first stage, simulation results were obtained using Matlab. Afterwards, an experimental implementation for each controller was carried out using LabVIEW based virtual instruments; a comparison between the functionalities of the presented controllers is done. Finally, the neural control system was implemented in an FPGA and the performance was compared to the one implemented in the LabVIEW virtual instruments.

Parallel fault detection algorithm for grid-connected photovoltaic plants

In this work, we present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this paper is, therefore, to outline a parallel fault detection algorithm that can diagnose faults on the DC-side and AC-side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module's temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software.The results obtained indicate that the parallel fault detection algorithm can detect and locate accurately different types of faults such as, faulty PV module, faulty PV String, Faulty Bypass diode, Faulty Maximum power point tracking (MPPT) unit and Faulty DC/AC inverter unit. The parallel fault detection algorithm has been validated using an experimental data climate, with electrical parameters based on a 1.98 and 0.52 kWp PV systems installed at the University of Huddersfield, United Kingdom.

Diagnostic method for photovoltaic systems based on six layer detection algorithm

This work proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) plant. For a given set of working conditions, solar irradiance and PV modules’ temperature, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation (VI) LabVIEW software. Furthermore, a third order polynomial function is used to generate two detection limits (high and low limit) for the VR and PR ratios obtained using LabVIEW simulation tool.The high and low detection limits are compared with real-time long-term data measurements from a 1.1kWp and 0.52kWp GCPV systems installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lies out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function.The obtained results show that the fault detection algorithm can accurately detect different faults occurring in the PV system. The maximum detection accuracy of the algorithm before considering the fuzzy logic system is equal to 95.27%, however, the fault detection accuracy is increased up to a minimum value of 98.8% after considering the fuzzy logic system.

The impact of cracks on photovoltaic power performance

This paper demonstrates a statistical analysis approach, which uses T-test and F-test for identifying whether the crack has significant impact on the total amount of power generated by the photovoltaic (PV) modules. Electroluminescence (EL) measurements were performed for scanning possible faults in the examined PV modules. Virtual Instrumentation (VI) LabVIEW software was applied to simulate the theoretical IV and PV curves. The approach classified only 60% of cracks that significantly impacted the total amount of power generated by PV modules.