Developing Data Acquisition Research Articles

Design and Implementation of a Multi-Channel Temperature Measurement System Based on LabVIEW

As computer technology and instrument technology advance rapidly, both virtual instrument and Lab-VIEW have significantly grown. LabVIEW is extensively employed in the industrial and academic sectors for developing data acquisition systems, instrument control software, and analysis software. This paper presents the design of a multi-channel temperature measurement system based on a single-chip CPU and LabVIEW. The system consists of analog-to-digital conversion modules, temperature measurement modules, and data acquisition modules. The system uses the Nyquist-Shannon sampling theorem and the advantages of various digital filter types to obtain temperature uncertainty, including the compensation uncertainty of the reference junction. The system is of high quality in measuring accuracy, stability, scalability and other aspects. The complete system was inspected and verified using a superconducting magnetic energy storage device (SMES). The results demonized the ease of construction, practicality and precise regulation of the system. These results also show that temperature measurement devices are less than 0.1°C absolute error and are highly accurate, making them valuable for applications that require precise temperature measurements.

Development of a low cost data acquisition system and training of J48 algorithm for classifying faults in cutting tool

The personalized use of the single-point cutting tool plays an crucial role in the economy of machining operations. Eventually, any machining defect in the cutting tool leads to the deterioration of the complete machining activity. Such faults are not traceable to conventional condition monitoring practices. The characterization of such in-process tooling defects must be approached intelligently. This would also contribute to the “self-monitoring” requirement in Industry 4.0. In this regard, the following is an introduction to a supervised machine learning (ML) classifier for designing empirical classification models for monitoring of tool condition. During the process of turning performed on the lathe, the vibration responses in the time domain of various defective and sound configurations of the single cutting tool were collected. By designing an algorithm which is event-based in Python programming, additional statistical features were extracted. J48 algorithm was used to display the features in the decision tree (DT) for developing data acquisition systems (DAQ). The DT Classifier affords accuracy of as much as 95 % and resulted to be a satisfactory classifier algorithm. The approach of implementation is maximum appropriate for actual time implementation of fault diagnosis. The system is small, low cost and additionally portable.

STATE OF KNOWLEDGE DISCOVERY PROCESS MODELS AND FRAMEWORKS

INTRODUCTION
 The first knowledge discovery process models were developed in the late 1990s, almost three decades ago. Based on several surveys, researchers F. Martínez-Plumed et al. (Martinez-Plumed, F. et al., 2021) and Rotondo A., Fergus Q. (Rotondo, A., Quilligan, F., 2020) argue that CRISP-DM (Chapman, et al., 2000) remains the default standard for developing data acquisition and retrieval projects. In almost thirty years, the industry and technology has evolved, and data science is now the leading term. The knowledge discovery process has changed significantly since the inception of the CRISP-DM model. An area in which the CRISP-DM model does not work well enough is data-driven products, and most products nowadays are in fact data driven. The amount and complexity of data in applications suggests that data processing requires significant technical work on management and infrastructure. In the CRISP-DM model, data is included as a static unit in the middle of the process (Martinez-Plumed, F. et al., 2021), which means that the knowledge retrieval process needs to be viewed in the context of the knowledge retrieval framework in which the process is applied.
 MATERIALS AND METHODS
 To identify the state of knowledge discovery process models and frameworks, the authors adopted a systematic literature review approach on knowledge discovery process models and knowledge discovery frameworks to answer two research questions. Q1: what kinds of process models are available and what is the state of knowledge discovery process models? And Q2: what are the design principles that characterise knowledge discovery frameworks? To achieve this objective, research articles addressing process models and frameworks were analysed.
 RESULTS
 The knowledge discovery process models developed in the mid 90s are still being used in organisational data mining projects. Most data retrieval algorithms and tools stop at creating and delivering models that meet technical requirements. Models are being developed, but entrepreneurs are either not interested in them or do not know what to do next to add value to their business decisions. Knowledge discovery in organisations is mostly a closed process for solving optimisation problems starting with problem definition, framework or model development to the discovery of workable models designed to provide functioning business insights that can be linked to or integrated with business processes and systems. Obtaining information and hidden correlations from data has a growing trend in information systems; in order to provide better services to end users and support decision-making processes, as well as to acquire valuable knowledge, it is necessary to integrate and analyse the generated data sets from different domains. Multiple innovative knowledge discovery frameworks are being analysed in the research paper.
 DISCUSSION
 The heterogeneity of the definition of knowledge and the perception of its concept creates wide possibilities for interpretation. One of the knowledge definitions according to Ikujiro Nonaka and Hirotaka Takeuchi's (Nonaka, I., Takeuchi, H., 1995) theory of knowledge creation is the acquisition of new knowledge applied to its usefulness to a particular organisation by relating it to the social context in which the knowledge is created and used. Knowledge discovery process models are still widely used within organisations and there are multiple knowledge discovery framework proposals for various fields. The necessity and corresponding technological requirements for knowledge discovery frameworks remain open for discussion.
 Recommended future research directions involve the following points:
 
 Technical requirements of knowledge discovery frameworks that define the requirements of components.
 Security requirements for knowledge discovery frameworks.
 Personal data protection requirements for knowledge discovery.

IoT-based system for communication and coordination of football robot team

PurposeThe purpose of this paper is to develop an information system which is based on the Internet of things (IoT) and used to support the communication and coordination in a cooperative robot team.Design/methodology/approachThe architecture of the IoT applications for decision-making activities in a complex system is elaborated, the focus lies on the effective implementation of system interactions at the device-level. A case study is provided to verify system performances.FindingsThe IoT concept has been introduced in an information system of a football robot team to support the coordination among team players. Various sensors are used to collect data from IoT, and data are processed for the controls of robotic players to achieve the better performance at the system level. The field test has shown the feasibility and effectiveness.Research limitations/implicationsTo investigate how IoT can be utilized in an information system for making complex decisions effectively, the authors use the decision-support system for a football robot team to illustrate the approaches in developing data acquisition infrastructure, processing and utilizing real-time data for the communication and coordination of robot players in a dynamic competing environment. While the presented work has shown the feasibility of an IoT-based information system, more work are needed to integrate advanced sensors within the IoT and develop more intelligent algorithms to replace manually remote control for the operations of robot players.Practical implicationsThe proposed system is specifically for a football robot team; however, the associated approaches are applicable to any decentralized system for developing an information system to support IoT-based communication and coordination within the system in the real-time mode.Originality/valueThe exploration of IoT applications is still at its early stage, existing relevant work is mostly limited to the development of system architecture, sensor networks, and communication protocols. In this paper, the methods on how to use massive real-time data for decision-making of a decentralized team have been investigated, and the proposed system has its theoretical significance to developing other decentralized wireless sensor networks and decision-making systems.

Rotor Fault Diagnosis and Simulation Based on LabVIEW

A fault diagnosis system based on LabVIEW is researched to analyze the rotating machinery fault. By developing data acquisition, signal analysis, processing and simulation, pattern recognition and fault diagnosis modules, this system can automatically measure and identify the shaft orbit of a rotor and get fault results. A pattern recognition method on the basis of the invariant moment algorithm is studied with LabVIEW and used to recognize shaft orbit shapes. The experimental results show that the shaft orbit of a rotor can be identified through invariant moment calculation. Because the shape of the shaft orbit of the rotor is close to the ellipse the main fault is imbalance. The fault result is proved to be correct by spectrum analysis. This study is helpful to develop a online fault diagnosis system based on LabVIEW.

Development of software for spectral imaging data acquisition using LabVIEW

Developing data acquisition software is a major challenge in integrating a spectral imaging system. This paper presents the design and implementation of a data acquisition program using LabVIEW for a liquid crystal tunable filter based spectral imaging system (900–1700nm). The module-based program was designed in a three-tier structure. The image acquisition process, modelled by a finite state machine, was implemented in LabVIEW to control the spectral imaging system to collect hyperspectral or multispectral images. The collected spectral images were encoded in general format and could be further processed by other common spectral image analysis tools. In addition, the program could be used to observe band ratio images of the test object in real-time, collect spectral images after ensemble averaging, and select region of interest for spectral image acquisitions. This program is a useful data acquisition tool for the filter-based spectral imaging system. The design and implementation techniques described in this article could also be used to develop similar spectral image acquisition programs.

Data collection studies for the superconducting super collider

We report on progress in our program for developing data acquisition systems for experiments at the SSCL. Using the MODSIM II object-oriented discrete-event simulation language, we are studying the behavior and interaction of various front-end circuits, data-collection chips, data-gathering networks, multilevel triggers, and buffer implementations. Results include system dead time, throughput, and efficiency, as they are influenced by data rates and system architecture.