Equipment Tracking Research Articles

A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring.

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment's state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time-frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time-frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura-Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.

Mobile Equipment Tracking Based on Wi-Fi Technology: Case of An Open Pit Mine

Mobile Equipment Tracking Based on Wi-Fi Technology: Case of An Open Pit Mine

Performance Analysis of the Particle Swarm Optimization Algorithm in a VLC System for Localization in Hospital Environments

Localization in hospitals can be valuable in improving different services in medical environments. In this sense, an accurate location system in this environment requires adequately enabling communication technology. However, widely adopted technologies such as Wireless Fidelity (WiFi), Bluetooth, and Radio Frequency Identification (RFID) are considered poorly suited to enable hospital localization due to their inherent drawbacks, including high implementation costs, poor signal strength, imprecise estimates, and potential interference with medical devices. The increasing expenses associated with the implementation and maintenance of these technologies, along with their limited accuracy in dynamic hospital environments, underscore the pressing need for alternative solutions. In this context, it becomes imperative to explore and present novel approaches that not only avoid these challenges but also offer more cost effective, accurate, and interference-resistant connectivity to achieve precise localization within the complex and sensitive hospital environment. In the quest to achieve adequate localization accuracy, this article strategically focuses on leveraging Visible Light Communication (VLC) as a fundamental technology to address the specific demands of hospital environments to achieve the precise localization and tracking of life-saving equipment. The proposed system leverages existing lighting infrastructure and utilizes three transmitting LEDs with different wavelengths. The Received Signal Strength (RSS) is used at the receiver, and a trilateration algorithm is employed to determine the distances between the receiver and each LED to achieve precise localization. The accuracy of the localization is further enhanced by integrating a trilateration algorithm with the sophisticated Particle Swarm Optimization (PSO) algorithm. The proposed method improves the localization accuracy, for example, at a height of 1 m, from a 11.7 cm error without PSO to 0.5 cm with the PSO algorithm. This enhanced accuracy is very important to meet the need for precise equipment location in dynamic and challenging hospital environments to meet the demand for life-saving equipment. Furthermore, the performance of the proposed localization algorithm is compared with conventional positioning methods, which denotes improvements in terms of the localization error and position estimation.

Optimising unplanned waste collection: An IoT‐enabled system for smart cities, a case study in Tangier, Morocco

AbstractAn innovative approach to the collection of unplanned municipal waste through the integration of an Internet of Things (IoT) enabled system in urban settings is presented. Despite significant strides in waste management optimisation, traditional systems have largely overlooked the management of occasional or seasonal waste such as green waste, wild dump, and construction debris. The authors seek to address this gap by deploying an IoT‐enabled system to optimise resource utilisation and efficiency. Building on existing infrastructures for real‐time tracking of waste collection circuits, equipment, and bin filling levels, the system incorporates an additional module to manage unpredictable waste categories. The system collects field data leveraging existing resources with minimal investment. To manage the sporadic nature of these waste types, the system employs a flexible approach with the use of sensors and algorithms for dynamic route planning and waste collection. Using the city of Tangier, Morocco, as a case study, a comprehensive methodology for waste location capture, GIS mapping, priority‐based route identification, scenario testing, and operational cost estimation is implemented. A modified version of the Contraction Hierarchies algorithm is applied to compute optimal waste collection paths, ensuring timely and efficient waste removal while minimising environmental impact. The findings from this research promise significant implications for municipal waste collection, particularly in developing countries, opening new possibilities for sustainable waste management practices in smart cities.

Angle-Based SLAM on 5G mmWave Systems: Design, Implementation, and Measurement

Simultaneous localization and mapping (SLAM) is a key technology that provides user equipment (UE) tracking and environment mapping services, enabling the deep integration of sensing and communication. The millimeter-wave (mmWave) communication, with its larger bandwidths and antenna arrays, inherently facilitates more accurate delay and angle measurements than sub-6 GHz communication, thereby providing opportunities for SLAM. However, none of the existing works have realized the SLAM function under the 5G New Radio (NR) standard due to specification and hardware constraints. In this study, we investigate how 5G mmWave communication systems can achieve situational awareness without changing the transceiver architecture and 5G NR standard. We implement 28 GHz mmWave transceivers that deploy OFDM-based 5G NR waveform with 160 MHz channel bandwidth, and we realize beam management following the 5G NR. Furthermore, we develop an efficient successive cancellation-based angle extraction approach to obtain angles of arrival and departure from the reference signal received power measurements. On the basis of angle measurements, we propose an angle-only SLAM algorithm to track UE and map features in the radio environment. Thorough experiments and ray tracing-based computer simulations verify that the proposed angle-based SLAM can achieve sub-meter level localization and mapping accuracy with a single base station and without the requirement of strict time synchronization. Our experiments also reveal many propagation properties critical to the success of SLAM in 5G mmWave communication systems.

Petrochemical Equipment Tracking by Improved Yolov7 Network and Hybrid Matching in Moving Scenes.

Petrochemical equipment tracking is a fundamental and important technology in petrochemical industry security monitoring, equipment working risk analysis, and other applications. In complex scenes where the multiple pipelines present different directions and many kinds of equipment have huge scale and shape variation in seriously mutual occlusions captured by moving cameras, the accuracy and speed of petrochemical equipment tracking would be limited because of the false and missed tracking of equipment with extreme sizes and severe occlusion, due to image quality, equipment scale, light, and other factors. In this paper, a new multiple petrochemical equipment tracking method is proposed by combining an improved Yolov7 network with attention mechanism and small target perceive layer and a hybrid matching that incorporates deep feature and traditional texture and location feature. The model incorporates the advantages of channel and spatial attention module into the improved Yolov7 detector and Siamese neural network for similarity matching. The proposed model is validated on the self-built petrochemical equipment video data set and the experimental results show it achieves a competitive performance in comparison with the related state-of-the-art tracking algorithms.

Enclosing contour tracking of highway construction equipment based on orientation-aware bounding box using UAV

Construction equipment tracking of highway construction site can obtain the spatiotemporal location in real time and provide data basis for construction risk control. The complete 2D moving of construction equipment in surveillance videos could be spatially represented by the translation, rotation and size change of corresponding images. To describe the temporal relationships of these variables, this study proposes a construction equipment enclosing contour tracking method based on orientation-aware bounding box (OABB), where UAV surveillance videos are employed to alleviate the occlusion problem. The method balances the rotation insensitivity of horizontal bounding box and the complexity of pixel-level segmented contour, which has three modules. The first module integrates OABB into a deep learning detector to provide detected contours. The second module updates OABBs with Kalman prediction to output tracked contours. The third module manages IDs of multiple tracked contours for construction equipment motions. Five in-situ UAV videos including 4325 frames were employed as the evaluation dataset. The tracking performance achieved 2.657 degrees in angle error, 97.523% in MOTA and 83.243% in MOTP.

Cleaning Quality Control Management of Medical Equipment in Hospital Disinfection Supply Room Based on Smart Medicine.

With the continuous improvement of medical level, the demand for medical devices increases year by year, and it is necessary to track the quality and safety of medical devices. Establishing the cleaning quality control management of medical devices can not only reduce medical accidents and inhibit the spread of counterfeit or substandard medical devices but also help enterprises find the source of problems, determine the flow of the same batch of products, and warn relevant enterprises to take measures after the occurrence of problem products, and ultimately ensure the rights and interests of consumers. This study aims to study the cleaning quality control management of medical equipment in the hospital disinfection supply room based on smart medical care and proposes related concepts of smart medical care, as well as related theories of Internet of Things technology and medical equipment tracking system and related concepts of medical equipment cleaning. In this study, by comparing the cleaning compliance rates of the two groups of medical devices through smart medical care, it can be seen that 100% of the research group applying program-based management is significantly higher than 76.6% of the conventional management path. There was statistical significance in the data comparison between groups (P < 0.05). Therefore, programmatic management is more targeted than conventional management paths.

Corona Detection and Power Equipment Classification Based on GoogleNet-AlexNet: An Accurate and Intelligent Defect Detection Model Based on Deep Learning for Power Distribution Lines

This paper presents a deep learning-based method for defect detection and classification of power distribution lines using video analysis. The first stage is dataset preparation in which different videos are recorded by a CoroCam 6D2 camera from distribution lines and labeled based on the defect type and the severity level of the defect. Then, a process is followed in such a way that a limited number of frames are used for processing, and power devices are detected in each frame using Faster R-CNN. The detector performance is improved by increasing the training dataset and changing the training hyper-parameters. Next, an equipment tracking technique is applied through the video frames. In the following, a method based on the classification of power equipment is presented. In this method, by corona color thresholding in all frames and applying a median filter over time, the connected components representing the corona are identified. Then, the frame containing the closest component to the component in the median image is selected. In the selected frame, the area around the component is cut and given to AlexNet or GoogleNet to determine the equipment type and the severity level of defect is determined. Also, the defective equipment phase is determined based on the insulators detected in the video. The proposed method not only performs better than the state-of-the-art but also is a practical method, and with the least dependence on environmental conditions, can automatically identify defects in distribution lines, even in videos containing several possible defective devices.

Development of an Automated Ticketing and Tracking System for the Monitoring of Surface Mine Hauling Operations

Telematics systems that integrate wireless communications and automation for equipment tracking and trip monitoring in mining operations have an advanced impact on the operational efficiency and optimization of the mine's asset inventory. In this study, a system was developed for solution in a mining area with low mobile network signal coverage and no internet service available to monitor and analyze the actual utilization executed by heavy equipment using microcontrollers with communication capabilities (GSM/GPRS, WiFi, and Bluetooth) and sensor modules for fuel monitoring and location tracking. Open-source software was utilized, such as PHP framework and MySQL, to provide a fast and real-time display of daily mine operations. The study provides an online tracking and ticketing system on mine hauling vehicles, introducing a cost-efficient, reliable, interactive platform for web-based monitoring and real-time telematics. The data acquired in real-time provide significant information which will allow innovative equipment optimization.

An intelligent monitoring system based on computer vision

Aiming at the customs supervision area of the comprehensive bonded area, this paper proposes an intelligent monitoring system based on computer vision through the research of computer vision, photoelectric sensors, digital image processing, data transmission network, automatic control and intelligent video analysis. We have realized the comprehensive supervision of people, vehicles, and things in the park, uploading all collected information to the centralized storage server, realizing uninterrupted data tracking, analysis, alarming, centralized management, unified scheduling, and command of front-end equipment. We have comprehensively improved the level of supervision of the comprehensive bonded area, provided the most intuitive information for emergency response, and performed the best response to emergencies according to the emergency plan to build a more intelligent comprehensive bonded area.

Intraoperative Radiation Exposure to U.S. Podiatric and Medicine Surgery Residents Annually: How Much Are We Really Getting?

As the number and complexity of operative techniques taught at U.S. podiatric medicine and surgical residencies (PMSR) with the added credential in reconstructive rearfoot and ankle (RRA) surgery has continued to increase, so to has the use of intraoperative fluoroscopy. The purpose of the present prospective observational pilot study was to quantify and compare the shallow dose equivalent (SDE), deep dose equivalent (DDE), and lens of the eye dose equivalent (LDE) exposures for podiatric medicine and surgery residents at a single PMSR-RRA over 12 consecutive months. Shallow-dose equivalent, DDE, and LDE exposures (in millirems) were measured using Landauer Luxel dosimeters from July of 2018 to July of 2019. Dosimeters were exchanged monthly, and mean monthly/annual SDE, DDE, and LDE exposures were calculated and compared. Overall, residents averaged 19 operative cases per month and 222 per year. More than half (53%) required intraoperative fluoroscopy, for which a mini C-arm was used in most cases. Monthly SDE, DDE, and LDE exposures averaged 7.3, 9.3, and 7.0 mrem, respectively; whereas annual SDE, DDE, and LDE exposures averaged 87.3, 112, and 84 mrem, respectively. No significant monthly (P = 1.0, P = .70, and P = .74) or annual (P = .67, P = .67, and P = .33) differences were identified between residents. The annual SDE, DDE, and LDE for residents at a single PMSR-RRA were well below the recommended dose limits of 50,000 mrem/year (SDE), 5,000 mrem/year (DDE), and 15,000 mrem/year (LDE) set by the National Council on Radiation Protection. However, given that the stochastic effects from low levels of ionizing radiation are cumulative, not well studied long-term, and relate both to the degree and duration of exposure, mini-C arm fluoroscopy, radiation tracking, and use of personal protective equipment provide simple means for residents to reduce any long-term potential for risk.

Energy-efficient long range wide area network for construction industry applications

Long Range (LoRa) technology is inevitable in the Internet of Things applications for the construction industry due to the large transmission range. Accordingly, this emerging technology is being used in many civil engineering applications such as water quality control, equipment tracking, etc., and the use of this technology is likely to multiply in the coming years. Therefore, the energy efficiency of this system should be investigated to reduce its environmental impact, which is lacking in previous investigations. This paper explores the effect of transmission parameters on power consumption and proposes a scheme for controlling transmission parameters to improve the energy efficiency of LoRa end nodes. The effectiveness of the proposed scheme is demonstrated through a case study on a real-time monitoring system based on LoRa for the compressive strength of concrete at early ages which is crucial for quality control and construction management.

Comparative Evaluation of Fleet Management Software in the Greek Construction Industry

Nowadays, construction companies are facing multiple difficulties on how properly and effectively manage their fleet of construction equipment. Fleet management (FM) is a feature that allows companies to avoid or minimize the risks associated with investing in equipment, efficiency, productivity, overall transportation costs, impartial compliance in legislation. FM can include a variety of functions such as equipment acquisition, maintenance, licensing, accident management, replacement, equipment tracking, health and safety management, and many others. Internationally, there have been developed many Fleet Management Software (FMS) that offer a company with several functionalities. In this research the functions of the most globally used FMS are analysed and evaluated according to their qualitative characteristics. A questionnaire was distributed among significant large construction companies and public construction institutions in Greece, in order to evaluate those characteristics. This allowed to identify their extent of use and to highlight the most important abilities that the users desire. Finally, these characteristics were ranked using the Relative Importance Weight (RIW). By this hierarchy process users’ needs are revealed, and further areas of software development are enlightened.

User Equipment Tracking for a Millimeter Wave System Using Vision and RSSI

In a mobile millimeter wave (mmWave) communication system, blockages cause disconnections or serious degradation of communications. Several techniques have been proposed to control radio links between multiple base stations based on blockage prediction using camera images to avoid these problems. However, blockage prediction requires continuously determining the position of user equipment (UE) with decimeter precision, which is difficult when sensors and resources on the UE side are not available, and there are many moving objects around the UE. To resolve this problem, we propose a UE tracking method that uses a received signal strength indicator (RSSI) and RGB-D camera images from the base station. The proposed method consists of a combination of visual tracking and reidentification of the UE using radio information and camera images. We use the synchronization of the RSSI variation with the occlusion on the image by movements of the UE and objects for reidentification. We evaluated the proposed method experimentally in an outdoor environment by simulating a communication area formed by mmWave band base stations. The proposed method achieved an 11.5% improvement in tracking accuracy compared with conventional visual tracking.

The Medical Device Unique Device Identifier as the Single Source of Truth in Healthcare Enterprises – Roadmap for Implementation of the Clinically Integrated Supply Chain

Documentation and tracking of supplies, equipment and medical devices is central to operational, financial, and clinical aspects of safe, efficient, and effective patient care. The labeling of medical devices with a unique device identifier (UDI) creates the opportunity to tightly integrate device information across health information systems by using the UDI as the index “source of truth”. Across 3 hospitals of the Duke University Health System, we executed a comprehensive implementation of UDI-based device and supply information management in our cardiac catheterization and electrophysiology laboratories. Following are our key insights. Implementing a UDI-centric environment is a complex undertaking requiring integration of information systems, management processes, and clinical workflows involving leadership, inventory management, supply chain, clinical and billing teams. Implementation involves the domains of procedure documentation, electronic health records (EHRs), charge capture and billing, and interface and information technology systems, including information systems vendors. Replacing manual processes with electronic messages is not simply an exercise in programming information systems – successful execution requires orchestrated re-engineering of clinical and operational workflows. Our initiative resulted in a more efficient and effective supply chain, eliminated operational and clinical documentation errors, automated the posting of device implant data to the EHR, reduced clinician burden, improved charge capture, and produced a substantial financial benefit, with return on investment recognized in well under 1 year. We believe our stepwise approach to accomplishing a clinically integrated supply chain can serve as a roadmap for other healthcare enterprises to follow.

Supplies and Equipment Inventory, Monitoring and Tracking Management System using Data Mining Techniques

In the present time, there are lot of web and software developer who provides different types of databased and online system to ease the burden of the different supply officer’s/inventory officers of different companies and government sectors but Cagayan State University is one of the big universities that remained inventory management in a manual way. This study together with the development of SEIMTMS was conducted to innovate the current system used and to abolish the difficulties and challenges encountered by the Supply Office staffs in inventory management, record keeping, monitoring and tracking, and report generation. Classification and clustering techniques were utilized to produce information and comprehensive decision support reports that aids the Supply Officer and University administration on decision- making and budget allocation. Furthermore, the system used Clustering technique together with MFP algorithm to forecast the frequently purchased supplies and frequently repaired equipment. These decision support reports are essential for Office Heads in identifying items to be purchased for a particular quarter. With the use of ISO/IEC 25010:2011 Software Quality Standards, the system was evaluated by IT Experts with a mean 4.67, qualitatively described as “very graet extent”.

Business process reengineering in health-care sector: application for the central sterilization unit

PurposeIn a rapidly changing environment, many health-care systems are forced to survive with rising costs and the need for proper resources utilization. To address the requirements for effective strategic planning, business process reengineering (BPR) is inevitable for health-care systems. This study aims to focus on the reengineering of central sterilization unit’s business processes to obtain performance improvement and efficiency. With the obtained new process design, it is expected to get standardization in all level of the central sterilization unit.Design/methodology/approachThis paper presents a BPR method for modeling the system in health-care sector to improve the productivity of the sterilization process of surgical equipment. The case study is conducted in a large hospital in Istanbul, Turkey. First, “as-is” process is modeled and the problems are determined. Then, solution methods are developed and the target system (to-be process) is redesigned and the workflows of each phase are remodeled by using ARIS tool. Enterprise resource planning (ERP)-driven BPR is used to reengineer the current processes and the future processes are operated through the modeled ERP system. Finally, by using a statistical analysis, performance of the new process is compared with the existing process using the first data obtained from the case hospital.FindingsIn many sterilization services, the control procedures are rarely used. Thus, an ERP solution is developed to automate sterilization tasks and track down the information flow. With the ERP solution, it is possible to track in which dates, where and by whom the items are processed. On the other hand, in the sterilization process, the sets are used efficiently according to the last sterile dates of the items. Also, with the ERP system, it is possible to reduce unnecessary paper usage and this gives the hospitals the opportunity to be more ecological and sustainable. As a result, a centralized sterilization process with an IT support provides efficiency, economy and patient safety.Originality/valueThis study seeks for the potential benefits of an ERP implementation in health-care sector. As a result of this project, improvements in productivity, cost and quality are expected in sterilization processes because the new process design will bring automation, human-related error reduction and equipment tracking system. For this purpose, a business process management software is developed and implemented to the central sterilization unit so that the information flows more safely and the managers can track the results quickly by charts and reports.

Classification and analysis of deep learning applications in construction: A systematic literature review

In recent years, the construction industry has experienced an expansion in the multitude of projects and emergent information. With the advent of deep learning, new opportunities have emerged for utilizing this vast amount of data to solve construction-related issues. While the use of deep learning has been increasing in construction, there has been no review on these applications to date. Therefore, this paper presents a Systematic Literature Review on the use of deep learning applications in construction. A total of 80 journal papers were identified and analyzed. Among these papers, six application-based topics were identified: equipment tracking, crack detection, construction work management, sewer assessment, 3D point cloud enhancement, and miscellaneous topics. Analysis shows that deep learning has been beneficial in leveraging data in areas such as crack detection and segmentation of infrastructure and sewers; equipment and worker detection and; and analysis and reporting on construction-related operations. Additionally, a discussion of the various deep learning techniques is provided as well as a contrast between deep learning, machine learning, and artificial intelligence.

Evaluation of internet of things (IoT) application areas for sustainable construction

PurposeThis purpose of the study is to evaluate areas of application of internet of things (IoT) in the construction industry, with the view of increasing the level of usage of technology. This will help in understanding the areas where IoT can be applied in the construction industry for better improvement.Design/methodology/approachA quantitative approach was adopted for this study, and the adopted questionnaire was structured on a five-point Likert scale to elicit the opinion of respondents in the areas of application of IoT in the construction industry. The respondents included are quantity surveyors, land surveyors, builders, architects and engineers. Bar chart, mean item score, one sample t-test and Kruskal–Wallis H test were used in analyzing the retrieved data.FindingsThe results showed that building information modeling, construction management, remote usage monitoring, equipment services and repair, construction tools and equipment tracking are areas where IoT is mostly applied in the industry. Site monitoring is the only factor that has significant difference in the opinions of professionals, while others do not have. One sample t-test revealed that three factors out of 12 do not have significance attached by professionals.Originality/valueThe study gives insight into different areas where IoT can be applied in the construction industry. It also highlights how its application can be improved through workshops, training, seminar and conference for construction professionals to keep themselves abreast of information and communication technology trends, especially in the aspect of IoT. The IoT adoption helps in accomplishing sustainable infrastructural projects with more convenience.