On-site Equipment Research Articles

Development of a Cost-Driven, Real-Time Management Strategy for e-Mobility Hubs Including Islanded Operation

The installation of electric vehicle supply equipment (EVSE) increases demand and peak loads, potentially straining existing energy distribution infrastructure. Dispersed and inadequately planned placement of charging points (CPs) can disrupt the electrical grid, surpass contracted demand thresholds, and require infrastructure upgrades, thereby incurring unfeasible costs for Distribution System Operators (DSOs). In this context, it is necessary to recognize the role of business models in enabling effective electrification of the transportation sector. In response to these challenges, this paper introduces a novel e-mobility hub management strategy, tailored for implementation in the Brazilian context. The proposed strategy revolves around a microgrid configuration encompassing dispatchable and photovoltaic generation, a battery energy storage system (BESS), EVSE infrastructure, and local loads. Moreover, this centralized controller facilitates the implementation of dynamic pricing and demand-response mechanisms, integral to business models seeking to integrate EVSE into the distribution grid. To validate the efficacy of the proposed solution, hardware-in-the-loop (HIL) simulations of the microgrid system are conducted. These simulations, incorporating the centralized controller, serve as a tool for assessing system performance and viability before on-site equipment deployment. Finally, this paper concludes with the insights gleaned from test analysis and its discussion through a selection of the most expressive scenarios, including islanded and connected operation modes.

Multi-species identification and number counting of fish passing through fishway at hydropower stations with LigTraNet

Fishway monitoring can verify the effectiveness of the fishway, optimise the operation mode, and achieve scientific management of fishway operations. Traditional fishway monitoring approaches, hindered by their inefficiency and substantial disruption of fish, are ill-suited for long-term surveillance; thus, employing video monitoring coupled with object detection technology presents an alternative or complementary solution. However, challenges such as the constrained computational capacity of onsite equipment in fishways, complexities involved in model deployment, and sluggish pace of detection are significant hurdles. In this study, by utilising the YOLOv8n model as a benchmark, we engineered a cross-stage partial module with a single convolution (C1) module to replace the existing C2f module with the aim of enhancing performance. We replaced the conventional 2D convolutions in the bottleneck configuration with depthwise separable convolutions and integrated the SimAM module to extract the detailed characteristics of the fish species. By amalgamating LigObNet detection with the DeepSORT algorithm, we established LigTraNet, which is designed to enable precise tracking, identification, and counting of individual fish. The results showed that LigObNet exhibited the lowest complexity and fastest detection speed for underwater fish among similar object recognition and detection models. Compared with the benchmark YOLOv8n model, there were reductions of 8.9% in the network layers, 40.5% in the parameter count, 39.3% in the memory footprint, and 35.8% in the giga floating-point operations and a 38.1% improvement in the inference speed. LigTraNet achieved a total count accuracy rate of 91.8%, demonstrating superior species quantification capabilities over other models with minimal resource usage and rapid inference capabilities, thus offering enhanced practicality for deployment on devices in real-world engineering contexts. This represents a departure from traditional manual monitoring methods for assessing fishway effectiveness, revolutionising aquatic ecological monitoring tools and methodologies and fostering the collaborative advancement of water resource project operations and ecological conservation.

Identifying potential concerns on surface water resources usage through citizen scientific field investigation in the Province of Antique, Philippines

Abstract This research conducted a citizen scientific field investigation and water quality assessment of nine major rivers using only on-site equipment and a smartphone in the Province of Antique, Philippines. Potential issues concerning water resources management in the province were discussed with local governors and citizens while conducting the water sampling and on-site measurement. The results of water quality assessments revealed that some of the rivers have experienced high turbidity, caused by anthropogenic activities such as embankment, sand mining, and dredging. In highly populated basins, such as the Sibalom and Malandog Rivers, a high biochemical oxygen demand level (>7 mg/L) was confirmed by the water quality analysis using an iPhone. Since high consumption of groundwater has already affected the lives of people in the province, surface water usage shifting from groundwater, involving the construction of a facility treating physical and chemical parameters, is needed. The citizen scientific approach employed in this research can provide more realistic insights into local environmental concerns, which would not be possible through quantitative measurements of water quality.

Meet the authors: Georgios Rizos, Jenna L. Lawson, and Björn W. Schuller

In their recent publication in Patterns, the authors proposed a methodology based on sample-free Bayesian neural networks and label smoothing to improve both predictive and calibration performance on animal call detection. Such approaches have the potential to foster trust in algorithmic decision making and enhance policy making in applications about conservation using recordings made by on-site passive acoustic monitoring equipment. This interview is a companion to these authors' recent paper, "Propagating Variational Model Uncertainty for Bioacoustic Call Label Smoothing".

Displacement of mining vibrating screen obtained from acceleration based on improved S–G filter

Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working conditions and equipment. In order to obtain accurate displacement signals of vibrating screen, the method for converting vibration acceleration to displacement based on improved Savitzky–Golay (S–G) filter is proposed. The Particle Swarm Optimization (PSO) algorithm is used to optimize the window length of the S–G filter with the fixed polynomial. The filters are cascaded to denoise the signals multiple times. The reasonable regularization parameter of the Smoothed Prior Approach (SPA) is calculated to remove the trend item from the acceleration signals. The vibration displacement is obtained by integrating the preprocessed acceleration data in the frequency domain. The results demonstrate that the objectivity of parameter selection of filter is improved, and the denoising effect is significant. The filtering effect of the filter is further improved after cascading. It becomes better as the number of stages of cascade increases. The vibration displacement can be obtained accurately by the proposed method. The vibration test platform is built to verify the correctness of the method.

Preliminary study on the diagnosis of NK stress based on the puncture mechanical characteristics of cucumber stem

To investigate the relationship between stem puncture mechanical characteristics and NK stress diagnosis, the microstructure, surface morphology, cellulose and lignin content, puncture mechanical characteristics, and epidermal cell morphology of cucumber stems were measured herein. The results indicated that the middle stem, which had a diameter of approximately 7000 μm, was more suitable for puncturing due to its lower amount of epidermal hair, and its gradual regularity in shape. Further, the cucumber stems were protected from puncture damage due to their ability to rapidly heal within 25 h.. The epidermal penetration of the cucumber stems increased with the increase in cellulose and lignin, though cellulose played a more decisive role. The epidermal break distance increased with an increase in N application and decreased with an increase in K+ application, but the change in intercellular space caused by K+ supply was the most critical factor affecting the epidermal break distance. In addition, a decrease in K+ concentration led to a decrease in epidermal brittleness, whereas the factors affecting epidermal toughness were more complex. Finally, we found that although the detection of epidermal brittleness and toughness on nutrient stress was poor under certain treatment, the puncture mechanical characteristics of the stem still had a significant indicative effect on N application rate. Therefore, elucidating of the relationship between the puncture mechanical characteristics of the stems and crop nutritional stress is not only beneficial for promoting stem stress physiology research but also for designing on-site nutritional testing equipment in the future.

Research on the Influence of Pantograph Catenary Contact Loss Arcs and Zero-Crossing Stage on Electromagnetic Disturbance in High-Speed Railway

During train travel, various factors, such as body vibration, uneven contact lines, and hard spots on carbon sliding plates and over electric neutral zones, often lead to brief separation between the pantograph and the contact line, i.e., the pantograph catenary contact loss phenomenon. With the continuous increase in train speed and traction power, the probability of pantograph catenary contact loss occurrences rises with a gradual increase in the energy of electromagnetic radiation, making the pantograph catenary arc a primary source of interference affecting the electromagnetic safety of high-speed railways. Understanding the mechanism, characteristics, and influencing factors of electromagnetic interference caused by pantograph catenary contact loss discharges is of utmost importance for analyzing and resolving on-site equipment interference faults. Our analysis of the physical process of pantograph catenary contact loss reveals that when the distance between the pantograph and catenary is significant and the duration is lengthy, high-voltage breakdown occurs within the pantograph catenary gap as it comes close again after the complete extinguishing of the arc. To investigate the electromagnetic radiation characteristics resulting from high-voltage breakdown discharge arcs in the pantograph catenary contact loss process, we established a laboratory test platform for assessing the electromagnetic disturbance characteristics of high-voltage pantograph discharge. We designed a test procedure utilizing fixed-gap breakdown discharge to evaluate the impact of the arc zero-crossing stage on electromagnetic radiation disturbances. Our research indicates that when the pantograph catenary spacing remains constant, an increase in voltage level leads to an elevation in the current within the discharge circuit, resulting in an increased intensity of impulse radiation generated during pantograph catenary contact loss events. During the moment of gap breakdown, the antenna records the highest amplitude of electromagnetic radiation. Also, during the steady-state arc ignition phase of the pantograph catenary gap, the zero-crossing stage generates pulsed discharge currents within the circuit, accompanied by substantial electromagnetic radiation. As the arc current increases, the zero-crossing time shortens, and the pulse current during the zero-crossing process decreases, accompanied by a reduction in the excited electromagnetic radiation. These observations reveal novel characteristics of electromagnetic radiation disturbances during steady-state arc ignition. The outcomes of our study provide valuable insights that can contribute to our understanding of the characteristics and influencing factors of electromagnetic radiation in pantograph catenary contact loss discharges and offer theoretical guidance for the resolution of pantograph catenary contact loss interference faults.

Blockchain-Based Lightweight Authentication Mechanisms for Industrial Internet of Things and Information Systems

The industrial internet of things (IIoT) necessitates robust cross-domain authentication to secure sensitive on-site equipment data. This paper presents a refined reputation-based lightweight consensus mechanism (LRBCM) tailored for IIoT's distributed network structures. Leveraging node reputation values, LRBCM streamlines ledger consensus, minimizing communication overhead and complexity. Comparative experiments show LRBCM outperforms competing mechanisms. It maintains higher throughput as the number of participating nodes increases and achieves a throughput approximately 10.78% higher than ReCon. Moreover, runtime analysis demonstrates LRBCM's scalability, surpassing ReCon by approximately 12.79% with equivalent nodes and transactions. In addition, as a combination of LRBCM, the proposed distributed lightweight authentication mechanism (ELAM) is rigorously evaluated against the security of various attacks, and its resilience is confirmed. Experiments show that ELAM has good efficiency while maintaining high security.

Smart Home System With Battery Backup and Internet of Things

This research enhances Smart Home Systems by integrating an Automatic Transfer Switch (ATS) for seamless power source switching between the grid and a backup battery, ensuring uninterrupted operation during power disruptions. An Automatic Battery Charging (ABC) system optimizes battery charging based on its condition, improving energy storage and efficiency. The system provides on-site electrical equipment control and sensor data access via a Human Machine Interface (HMI). Remote monitoring and control through the Blynk app offer convenience. Additionally, an energy consumption estimation feature allows users to estimate billing costs, with the Battery State of Charge (SoC) indicating the remaining battery capacity. Hardware testing showed the system's reliability with a 2-4 second ATS response and ±2-second ABC response. This research offers homeowners reliable power continuity and energy optimization. It contributes to IoT-based smart home systems, demonstrating ATS and ABC effectiveness, advancing both theory and practice for modern smart living.

Denoising of blasting vibration signals based on CEEMDAN-ICA algorithm.

Monitoring of blasting vibration signals can make the collected blasting signals noisy due to various factors such as on-site actual construction conditions, equipment, and instruments. Thus, the acquired signals should be preprocessed before analyzing the blasting vibration signals. The current study proposes a blasting vibration denoising method based on CEEMDAN-ICA to alleviate the noise component in the blasting signals effectively. The collected signal is first decomposed through the CEMMDAN algorithm to extract the IMF components of different frequency bands. Next, the collected signal is estimated using the ICA algorithm to attain corresponding ICA components. Finally, the arrangement entropy of the ICA components is calculated for signal reconstruction to attain a small noise blasting vibration signal. Simulations are performed to evaluate the feasibility of the presented algorithm and compare its efficiency with the traditional algorithms. The results demonstrate that this algorithm has specific advantages over other algorithms, which can more accurately denoise the original signal and retain the effective signals, providing a new denoising method for subsequent signal analysis.

A time series and deep fusion framework for rotating machinery fault diagnosis

For shafting rotation equipment, in the fault diagnosis based on vibration analysis, the sampling signal is a waveform of a short moment, which is quasi-static information at the corresponding moment. A single quasi-static piece of information does not necessarily contain obvious fault information, however, in a longer period much larger than the sampling period, the time series information composed of multiple quasi-static combinations may well-characterize the fault. Traditional deep learning algorithms often focus on fault features in quasi-static information, ignoring time series features, which results in low diagnostic accuracy. To solve the above problems, this paper firstly presents a fault diagnosis framework of time series and deep fusion network (TDFN). Then, a method to extract time series information and quasi-static information based on expert experience knowledge is proposed, and finally studies the method of using this framework to train the network and fuse the two types of features. Different from existing deep learning methods, TDFN includes time series blocks, depth blocks and fusion blocks. This paper uses laboratory data and industrial rotor data for verification, the results show that the overall accuracy of TDFN reached 93.8% and it does not experience a drop in accuracy after fusion. The diagnosis accuracy is higher than traditional networks, providing guiding suggestions for the management and operation of on-site industrial equipment.

A cooperative model to lower cost and increase the efficiency of methane leak inspections at oil and gas sites

Methane is a potent greenhouse gas that tends to leak from equipment at oil and gas (O&G) sites. Conventional leak detection and repair methods for fugitive methane emissions are labor-intensive and costly because they involve time-consuming close-range, component-level inspections at each site. This has prompted duty holders to examine new methods and strategies that could be more cost-effective. We examined a cooperative model in which multiple duty holders of upstream O&G sites in a region use shared services to inspect on-site equipment using optical gas imaging camera or Method 21. This approach was hypothesized to be more efficient and cost-effective than independent inspection programs by each duty holder in the region. To test this hypothesis, we developed a geospatial simulation model using empirical data from 11 O&G-producing regions in Canada and the United States. We used the model to compare labor cost, transit time, mileage, vehicle emissions, and driving risk between independent and co-op leak inspection programs. The results indicate that co-op leak inspection programs can generate relative savings in labor costs (1.8%–34.2%), transit time (0.6%–38.6%), mileage (0.2%–43.1%), vehicle emissions (0.01–4.0 tCO2), and driving risk (1.9%–31.9%). The largest relative savings and efficiency gains resulting from co-op leak inspection programs were in regions with a high diversity of duty holders, which was confirmed with simulations of fictitious O&G sites and road networks spanning diverse conditions. We also found reducing leak inspection time by 75% with streamlined methods can additionally reduce labor cost by 8.8%–41.1%, transit time by 5.6%–20.2%, and mileage by 2.60%–34.3% in co-op leak inspection programs. Overall, this study demonstrates that co-op leak inspection programs can be more efficient and cost-effective, particularly in regions with a large diversity of O&G duty holders, and that methods to reduce leak inspection time can create additional savings.

Design of a Closed Piggery Environmental Monitoring and Control System Based on a Track Inspection Robot

To improve environmental quality in enclosed piggeries, a monitoring and control system was designed based on a track inspection robot. The system includes a track mobile monitoring platform, an environmental control system, and a monitor terminal. The track mobile monitoring platform consists of three main components: a single-track motion device, a main box containing electronic components, and an environmental sampling device. It is capable of detecting various environmental parameters such as temperature, humidity, NH3 concentration, CO2 concentration, light intensity, H2S concentration, dust concentration, and wind speed at different heights below the track. Additionally, it can control on-site environmental control equipment such as lighting systems, ventilation systems, temperature control systems, and manure cleaning systems. The networked terminal devices enable real-time monitoring of field equipment operating status. An adaptive fuzzy PID control algorithm is embedded in the system to regulate the temperature of the piggery. Field tests conducted on a closed nursery piggery revealed that the system effectively controlled the maximum temperature range within 2 °C. The concentrations of CO2, NH3, and PM2.5 were maintained at a maximum of 1092 mg∙m−3, 16.8 mg∙m−3, and 35 μg∙m−3, respectively. The light intensity ranged from 51 to 57 Lux, while the wind speed remained stable at approximately 0.35 m∙s−1. The H2S concentration was significantly lower than the standard value, and the lowest relative humidity recorded was 18% RH at high temperatures. Regular humidification is required in closed piggeries and other breeding places when the system does not trigger the wet curtain humidification and cooling function, as the relative humidity is lower than the standard value. By controlling the temperature, the system combined with a humidification device can meet environmental requirements. The control method is simple and effective, with a wide range of applications, and holds great potential in the field of agricultural environmental control.

Preliminary Design and On-Site Testing Methodology of Roof-Cutting for Entry Retaining in Underground Coal Mine.

Entry retaining via roof cutting is a new longwall mining method that has emerged in recent years, and is characterized by high resource utilization and environmental friendliness. Due to the complexity of this method, a field study is commonly employed for process optimization. Roof blasting is a key operation for retaining the entry, and the current practice involves dynamically adjusting blasting parameters through on-site testing and postblasting monitoring. However, the existing literature lacks detailed descriptions of blasting operations, making it difficult for field engineers to replicate the results. In this study, based on a roof cutting project for entry retaining, a preliminary design of blasting parameters is made based on theories and on-site geological conditions. The on-site test methods and equipment for roof-cutting blasting are described in detail, and the fractural patterns under different blasting parameters are analyzed. After the retreat of the working face, the state of roof caving in the goaf is analyzed based on monitoring data, and the effectiveness of top cutting is evaluated through reverse analysis, leading to dynamic adjustments of the blasting parameters. This research provides a reproducible construction method for roof-cutting operations and establishes the relationship between blasting parameters and post-mining monitoring data. It contributes to the development of fundamental theories and systematic technical systems for entry retaining via roof cutting, offering high-quality case studies for similar geological engineering projects.

Cost and Time Optimization in Fleet Management- Case Study of Sinnar Shirdi Highway

Abstract: The aim of the study is to show how fleet management can help in saving time and cost by comparing the current onsite equipment to optimised results. The detailed study of fleet of equipment and their related parameters and specifications is important because it helps to determine requirement of equipments for a particular activity. All the cost including labor, maintenance, operating costs, machine capacity were considered to find out the productivity as well as expenses. Based on the site visit and data collection various parameters such as productivity of excavator, production rate, cycle time and other parameters of activities such as earthmoving equipment, hauling of trucks as well as productivity of paver were also calculated. The parameters were obtained for various combinations of different truck sizes as shown in the MS- Excel sheet. For different truck sizes and different combinations we get different cost index.

Safety Production Detection Algorithm of Smart Factory Based on PC Control Technology

Abstract In order to obtain the best scheme of safety production and operation decision-making of a smart factory and improve the rapid response ability of product information of a smart factory, the safety production detection algorithm of a smart factory is designed by using programmable computer (PC) control technology. The main interface, login interface, function interface, and navigation interface are designed, respectively, to realize the front-end design of the smart factory safety production detection platform. Based on PC control technology, the hardware equipment of perception layer, network layer, and application layer are installed, and on this basis, the database of the smart factory safety production detection platform is established. Once the operation data information is collected and detected, it is stored to the equipment fault experience knowledge base, and the fault diagnosis method of the minimum cut set of the fault tree is used to implement the on-site equipment diagnosis of the underlying Internet of Things of the smart factory, and finally the information sharing function of safety production detection through the information transmission of the safety production links of each smart factory is realized. Experiments show that the throughput data of the designed algorithm run stably, improve the control accuracy of safety production, and effectively improve the work efficiency.

Developing predictive models of construction fatality characteristics using machine learning

Construction fatalities have significant economic and emotional burdens to construction employees, families, and organizations. Understanding critical factors influencing construction fatalities and eventually developing predictive models to predict construction fatality characteristics are therefore important. Such activities, which are traditionally based on questionnaire and simple statistical analysis, can now be conducted using comprehensive datasets on construction fatality and advanced machine learning approaches. This study aims to develop predictive models of construction fatality characteristics, including nature of injury (NOI), part of body (POB), source of injury (SOI), and event or exposure (EOE) using machine learning approaches. 30 explanatory variables from 694 fatalities reported by the National Institute for Occupational Safety and Health from 1982 to 2014 are used to build the predictive models, with prediction accuracy of 56.6%, 54.0%, 76.5% and 84.9% for NOI, POB, SOI, EOE respectively. Specifically, the model has a prediction accuracy of 84.7% for construction fall fatalities. Important indicators for predicting SOI and EOE are largely the same, with the most important ones being the likelihood of fall, PFAS (personal fall arrest system, including its functionality and relevant training), workers’ activity, onsite safety equipment and install safety protection. Similarly, important indicators for predicting NOI and POB include fall, PFAS, injury year, workers’ activity, location and safety equipment. The results will offer useful guidance for construction organizations to establish relevant emergency response plans and first aid facilities and services that correspond to the most likely NOI, POB, SOI and EOE on construction sites.

Exercise Habits and Resources for Southeastern US Firefighters.

This study aimed to assess exercise habits and facility resources accessible to southeastern US firefighters. Firefighters completed questionnaires on topics including demographics, work demands, exercise methods, and facility resources. Sixty-six percent of participants reported they engage in exercise ≥30 min/d. More firefighters engaged in exercise when better on-site equipment options were available ( P = 0.001). Perception of how on-shift exercise impacts occupational performance did not impact their on-shift exercise ( P = 0.17). Although 34% reported not meeting exercise guidelines, the majority of southeastern US firefighters reported meeting these guidelines and allotted exercise time on-shift. Exercise habits are impacted by equipment options, but not call volume or perception of on-shift exercise. Open-ended question responses suggested that perception of on-shift exercise did not deter firefighters from exercising on-shift but may impact intensity.

Prediction of Storage Conditions to Increase the Bioenergy Efficiency of Giant Miscanthus Pellets Produced through On-Site Integrated Pretreatment Machines

Fossil fuels are associated with problems such as resource depletion and pollution, necessitating the exploration of alternatives. Giant miscanthus (Miscanthus × giganteus Greef et Deu), a perennial that can be harvested yearly, requires a low production energy input. It has less ash content and high heat efficiency and has attracted attention as an energy source. An on-site processing equipment, powered via a tractor and equipped with a chipper and a two-stage compression roller, was developed that can harvest 1000 kg of giant miscanthus per hour and simultaneously produce compressed pellets eliminating unnecessary processes such as transportation and processing. With its use, 33–74.5 kWh/t of electrical energy can be saved by producing pellets. The changes in moisture content between the produced compressed pellets and two samples of the ground product were measured immediately before compression for 24 h at relative humidity ranging from 65% to 80%. The moisture content was 6% initially; it ranged from 6.71% to 7.81% in compressed pellets, depending on the conditions, and from 7.44% to 9.82% in the ground sample immediately before compression, indicating the effect of the physical form of the biomass and humidity in the environment. The possible storage period (while maintaining the moisture content at 8–10% for optimal biofuel efficiency based on the measured data) was predicted. The optimal relative humidity of the storage environment for maintaining biomass quality for more than 6 months was predicted to be ≤77% and ≤70% for the compressed pellet and ground sample, respectively. Moreover, at a relative humidity ≥77%, giant miscanthus biomass, immediately before compression, had >10% moisture content in 2 days, warranting caution in storage.

Qualitative and quantitative study to assess the use of rammed earth construction technology in Perth and the south-west of Western Australia

During the last decades, rammed earth technology has been explored worldwide because of a growing concern for sustainable constructions. A relevant, contemporary technical culture of rammed earth developed in the south-west of Western Australia (WA) from the 1970 s. Several examples of residential, educational, commercial and community buildings in the region were built in rammed earth during the last 40 years. An active group of professionals involved in rammed earth construction operates in the region. This study assesses the state of the art of rammed earth construction practice in the south-west of WA based upon a qualitative and quantitative questionnaire to the professionals involved in the local rammed earth construction industry. The questionnaire collects data on five major technical areas: (i) strengths and weaknesses of rammed earth technology; (ii) opportunities and deterrents to employ rammed earth in the south-west of WA; (iii) rammed earth materials and on-site equipment and construction process; (iv) aesthetic and design qualities of rammed earth buildings; and (v) thermal performance of rammed earth buildings. The study's objective is to analyse the potential of the rammed earth technology within the local construction sector, engaging the local industry to identify the paths for further development of the current guidelines.