Research on data acquisition method for intelligent instrument based on OPC communication

The purpose of this design is to design a WIFI based intelligent meter data acquisition module; it is mainly composed of HT6015 chip, WIFI module, etc. The voltage and current signals are first entered into the relevant resistive and capacitance filter processing signal, filter clutter and so on, and then transmitted to the RN8027C metering chip to realize the conversion of electric energy into pulse signal, then transmitted to the HT6015 chip to complete the statistics of pulse signal and then to complete the statistics of electric energy. Finally, the power information is transmitted by the WIFI module. The electrical signals in this system are first passed through the capacitance resistor to divide the voltage to produce the equal proportion signal, and are easily adjusted to the input range of the sampling port of the metering chip. The low pass filter is used to filter out the interference signal containing the useful signal, so as to obtain the real power consumption and other information more accurately. After RN8027C the chip, the electrical signal to the pulse signal is transformed, and then the electrical information can be obtained by processing the statistical change of the HT6015 chip, and the WIFI module can be transmitted. As a result, the electronic signal is collected, processed and transmitted quickly, which is the design of the intelligent meter data acquisition module based on WIFI.

The thesis starts from the basic theory, introduces the method of physical data acquisition and the concept of data mining. Marketing evolves from traditional marketing schemes to data mining, which leads to the relationship between data mining technology and modern market management. It focuses on the method of obtaining user information and data mining technology in "cross-marketing", "integrated marketing", and "integrated marketing". Applications in "real-time marketing", "green marketing", "relationship marketing", and "network marketing". At the same time, combined with case analysis, the comprehensive application of data mining in "shopping basket analysis" is discussed and elucidated from the theoretical and technical level.

Recently, a novel data acquisition method has been proposed and experimentally implemented for differential phase‐contrast computed tomography (DPC‐CT), in which a conventional X‐ray tube and a Talbot‐Lau‐type interferometer were utilized in data acquisition. The divergent nature of the data acquisition system requires a divergent‐beam image reconstruction algorithm for DPC‐CT. This paper focuses on addressing this image reconstruction issue. We have developed a filtered backprojection algorithm to directly reconstruct the DPC‐CT images from acquired fan‐beam data. The developed algorithm allows one to directly reconstruct the decrement of the real part of the refractive index from the measured data. In order to accurately reconstruct an image, the data need to be acquired over an angular range of at least 180∘ plus the fan angle. As opposed to the parallel beam data acquisition and reconstruction methods, a 180∘‐rotation angle for the data acquisition system does not provide sufficient data for an accurate reconstruction of the entire field of view. Numerical simulations have been conducted to validate the image reconstruction algorithm.

This paper outlines the three main field bus type standards: FOUNDATION Fieldbus; WorldFIP; and the CAN bus, each of which have great advantages over traditional instrumentation networking technology. The paper shows, using their specification, how they improve traditional control and data acquisition methods. By analysing the main fieldbus types the paper shows how they are more robust, how they can be used to distribute data around the control system (rather than centralise it in traditional systems), provided increased diagnostic information, are easy to implement, fault confinement, and move simple control operations from the main controller to the local environment. This will help to segment industrial plants into zones which can control themselves. The main control will then be responsibility of high-level control, and interzone communications. The paper concludes by discussing how fieldbusses will answer many of the problems of previous control and data acquisition systems

At present, storage technology cannot save data completely. Therefore, in such a big data environment, data mining technology needs to be optimized for intelligent data. Firstly, in the face of massive intelligent data, the potential relationship between data items in the database is firstly described by association rules. The data items are measured by support degree and confidence level, and the data set with minimum support is found. At the same time, strong association rules are obtained according to the given confidence level of users. Secondly, in order to effectively improve the scanning speed of data items, an optimized association data mining technology based on hash technology and optimized transaction compression technology is proposed. A hash function is used to count the item set in the set of waiting options, and the count is less than its support, then the pruning is done, and then the object compression technique is used to delete the item and the transaction which is unrelated to the item set, so as to improve the processing efficiency of the association rules. Experiments show that the optimized data mining technology can significantly improve the efficiency of obtaining valuable intelligent data.

Background: In order to better realize the real-time on-line monitoring of the working state of the coal mill and determine the status of the equipment used in the coal mill, the intelligent embedded system of the coal mill based on DSP was studied. Methods: Firstly, the working requirement of the coal mill and the task of data acquisition system have been briefly introduced, and the requirement of the overall embedded system design has been put forward. Then, the structure design was analysed including both hardware and software, and the embedded system design including digital signal processing, and data acquisition was completed. All patents related to intelligent embedded data acquisition and storage system are described in detail. Results: After that, the function test of the actual coal mill embedded system was carried out, which proved the practicability and reliability of the designed embedded system for coal mill and based on DSP. Conclusion: The embedded system improves the automation level of coal mill, and is of great significance to the on-line monitoring and research of large-scale machinery and equipment in the future.

With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system.

Data quality plays an essential role in decision-making, as the latter may incorporate some risks in different application areas. In the context of industry 4.0, the amount, the versatility, and the speed of information flow for decision-making are important issues. The quality and, in particular, the dependability of data is paramount. This paper investigates the leading data quality characteristics in the industry 4.0 environment with the related issues due to various interactions. It proposes a taxonomy of data sources and flows, from acquisition to the information extraction level for decision-making. The authors highlight the specific issues of error and uncertainty propagation management as significant research challenges for designing intelligent data collection and acquisition systems for industrial manufacturing decision support within the framework of the new generation of industry development. They review data quality characteristics definition and assessment requirements, and suggest classifying these characteristics into four categories. Data quality assessment methods and models with relation to decision-making are also examined. They willfully left aside the investigation of data quality improvement processes for a future more detailed paper.

The rate of development of open government data (OGD) portals has been fast in recent years due to potential benefits of the utilization of OGD. However, scholars have emphasized lack of use as a key problem in the realization of these benefits. Although studies have been carried out to understand decisive factors in OGD utilization from the aspects of either portals or users, they failed to consider the interaction between the two. Therefore, our study carried out an analysis of the interaction between users and OGD portals during users’ data acquisition process from three aspects: data acquisition methods, data quality requirements, and helping functions. We carried out a survey in a Chinese population to collect data for analysis. Results show users’ high acceptance of keyword search as their method for data acquisition through OGD portals but browsing showed higher usage frequency and was a more stable data acquisition behavior. Females show better acceptance of regular recommendations (e.g. RSS) based on their visiting histories than males. Users’ age, education background and occupation affect their demands of different data quality attributes. Our analysis also shows positive relationship between users’ data acquisition habits with their demands of data quality, users’ need of help with their feelings of difficulties in using the portal, and users’ need of help with their demands of data quality. We suggest promoting OGD utilization by offering better helping functions and improving data qualities in future development of OGD portals.

Data from the distributed control system (DCS) or supervisory control and data acquisition (SCADA) system provide useful information critical to the evaluation of the performance and transportation efficiency of a gas pipeline system with compressor stations. The pipeline performance data provide correction factors for compressors as part of the operation optimization of natural gas transmission pipelines. This paper presents methods, procedures, and an example of model validation-based performance analysis of a gas pipeline based on actual system operational data. An analysis approach based on statistical methods is demonstrated with actual DCS gas pipeline measurement data. These methods offer practical ways to validate the pipeline hydraulics model using the DCS data. The validated models are then used as performance analysis tools in assessing the pipeline hydraulics parameters that influence the pressure drop in the pipeline such as corrosion (inside diameter change), roughness changes, or basic sediment and water deposition.

The basic principle of integrating a motor control center (MCC) and a distributed control system (DCS) is to connect the microprocessor based devices within the MCC to the DCS using digital communication. The control system can utilize this connection for supervisory control and data acquisition (SCADA) purposes. This paper describes one approach to this integration of equipment for a new industrial facility. The paper describes the various aspects involved in the application of microprocessor based devices within a low voltage MCC and its hardware connection to a DCS. The paper also discusses the software selected and the programming necessary to provide supervisory control and monitoring capability.

Nowadays, information is the most important factor for any manufacturing enterprise. Information helps a manufacturer to make decisions about management, production, operation, maintenance, and repair. Information technology is a key for enterprises who are realising integrated manufacturing all over the world. Data acquisition is the backbone of information technology. Data acquisition technology is the product of the fusion and interaction of many technologies. It is necessary to develop new data acquisition technology and methods by using the recently produced research results. It is also important for realising manufacturing automation, promoting product quality, and enhancing the competitive ability of an enterprise. In this paper, we first analyse three common kinds of data acquisition mode and their features used in manufacturing systems. Then, based on the background of manufacturing automation, agents and distributed artificial intelligent technology, which form a new data acquisition model framework suitable for an advanced manufacturing system, is proposed, and some details about this new model are discussed. Two examples of this model used in manufacturing systems are then given to show the availability and correctness of this model framework. We conclude with current research results in this area and give an outline of future work.

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of coal-fired utility boilers in this project but can be applied to many other industries and applications as well. This project leveraged the existing electrochemical noise-based monitoring system and the new sensor design is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data is transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

The main objective of this research program is to design, manufacture, and demonstrate a miniaturized, multi-process, monitoring system (mMPMS) for boiler condition management and easy system deployment to obtain a higher spatial resolution of wall conditions in utility boilers. This system will facilitate a Condition-Based Maintenance (CBM) philosophy that actively monitors the health of assets to predict and prevent failures and maximize availability and generating capacity at a reduced cost. CBM systems can provide boiler data that the advanced process control (APC) system can utilize for plant performance optimization, which is increasingly relevant as coal power plants shift from predominantly base-load operation to predominantly transient operation involving large load swings. The mMPMS is based on an electrochemical sensor that provides a real-time indication of the risk of damage to key locations in the radiant or convective section of a coal-fired boiler such as metal loss rates, heat flux, metal surface temperature, and deposit thickness. These indications can be utilized to optimize boiler performance as well as improve boiler availability in conjunction with corresponding operating conditions. This monitoring system was developed and tested in the high-temperature regions of a coal-fired utility boiler in this project but can be applied to many other industries and applications as well. The new sensor design, leveraging the existing electrochemical noise-based monitoring system, is small enough to be installed through the webbing of the waterwalls without the need for long shut-downs to bend tubes and to make it feasible to obtain high spatial resolution in the boiler. Data can be transferred to the plant distributed control system (DCS) and any other control system. The sensor body that houses the sensor assembly was designed to ensure good conductive contact with boiler tubes to ensure the sensor is held at an identical temperature to the tube surface temperature. The data acquisition and signal conditioning modules were redesigned into a small footprint with optimized cooling of the module. System software was developed specifically for the new signal conditioning module and is compatible with plant PLCs. After the preliminary testing at a pilot-scale facility, five mMPMS were installed at a full-scale pulverized coal-fired plant, Basin Electric Power Cooperative’s Leland Olds Unit 1. The systems were demonstrated over a 6-week period during typical operation. Sensor measurements of deposit thickness were validated during the demonstration and subsequently leveraged to determine sensor-based boiler cleaning strategies. These strategies have the benefit of reduced thermal stresses on boiler tubes from over-cleaning and improved boiler water management. At the end of the project, continued development of the sensor technology was carried out at PacifiCorp’s Hunter Station. REI leveraged the permanent installation of the mMPMS in Unit 3 made possible by DOE funding on a separate program. The work at Hunter Plant focused on application of machine learning and artificial intelligence-based models for integration of sensor signals into control and optimization of Hunter Unit 3 processes.

Customer data acquisition is an important task in data-driven business analytics. Recently, there has been a growing interest in the effective use of an organization’s internal customer data, also known as first-party data. This work studies the acquisition of new data for business analytics based on first-party data resource. We address issues related to both acquisition cost and data quality. To reduce acquisition cost, we consider using auction-based methods, such as the generalized second price (GSP) auction, for acquiring data with differential prices for different customers. We find that the GSP-based data acquisition method incurs a lower cost and/or achieves a higher response rate than fixed price methods. To maximize data quality, we propose novel optimization models for different data acquisition methods and data quality measures. The proposed models maximize the quality of the acquired data while satisfying budget constraints. We derive and discuss the solutions to the optimization models analytically and provide managerial insights from the solutions. The proposed approach is effective in increasing customer responses, reducing selection bias, and enabling more accurate estimation and prediction for business analytics. The results of the experimental evaluation demonstrate the advantage of the proposed approach over existing data acquisition methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0037 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0037 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .