Condition Of Equipment Research Articles

Health issues and management practices of pesticide exposure in Ethiopia, East Africa

Pesticides are commonly used in agriculture for pest control, but their improper usage and handling can lead to high farmer exposures, negative health effects, and pollution of water bodies. The purpose of this study was to investigate farmer’s perceptions of the effects of pesticide runoff on water bodies, and to describe their self-reported knowledge of appropriate pesticide use, methods of application, sources of information, and related health issues and management strategies in Ethiopia. Data were collected via structured questionnaire, which was administered as an in-person interview to 582 farmers who cultivated vegetables. To assess unsafe pesticide handling practices, researchers observed the storage of pesticides and the condition of personal protective equipment and asked farmers about their pesticide disposal methods. The study found that nearly 87.97% of farmers reported mixing pesticides using a stick or their bare hands. Most respondents (88.83%) mixed pesticides to spray just on their farmland, and some (10.14%) were near water sources. It revealed that farmers have a low level of awareness about the risks and effects associated with pesticides and do not follow safe practices. Out of the farmers who had utilized pesticides, 55.67% reported encountering health issues associated with pesticide exposure. To reduce exposure and health risks, the study suggests implementing comprehensive interventions, such as integrated pest management, safety training, and alternative control methods.

Application of Artificial Intelligence Techniques in Assessing the Safety of Lifting Equipment

The focus of the article is addressing the use of artificial neural networks in evaluating the probability of potential accidents occurring while operating lifting crane machinery. The information is obtained from telemetric readings collected by microcontroller load limiters, and also from the results of routine technical and daily equipment condition inspections, which may involve stochastic data.

Aggregation Modeling for Integrated Energy Systems Based on Chance-Constrained Optimization

Integrated energy systems (IESs) strengthen electricity–gas–heat multi-energy coupling and reduce wind and light abandonment. For grids with superior distribution, IESs are similar to virtual energy storage systems and are able to realize efficient interaction with the grid by synergizing the operating status of the internal equipment and improving the security, economy, and flexibility of the grid’s operation. However, the internal equipment coupling of an IES is complex, and determining how to evaluate its adjustable capacity range (that is, the upper and lower boundaries of its external energy demand) considering the uncertainty and volatility of wind power and photovoltaic output is a problem to be solved. To solve this problem, this paper presents a chance-constrained evaluation method for the adjustable capacity of IESs. Firstly, mathematical models and operational constraints of each device within the IES are established. Secondly, based on the mathematical model of chance-constrained planning, an adjustable capacity range assessment model considering the uncertainty of wind and photovoltaic output is established. Finally, the MATLAB/Yalmip/Gurobi solver is used for the optimization solution, and the adjustable capacity range interval of the constructed IES model is solved using an arithmetic example to analyze and verify the correctness and validity of the method and to study the influencing factors of its adjustable range.

Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system’s electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%.

Analisis Faktor Yang Mempengaruhi Keselamatan Operasional Kapal di Pelabuhan Merak

The study, titled "Analysis of Factors Affecting Safety at Merak Port," aims to identify and analyze key factors influencing operational safety at Merak Port. This port is a strategic route linking Java and Sumatra, serving millions of passengers and vehicles annually. However, high activity levels and operational complexity present significant safety challenges. This research employs a qualitative case study approach, combining interviews, field observations, and document analysis. Quantitative data were also collected via questionnaires distributed to operators, workers, and passengers. Findings reveal that human factors, weather conditions, and infrastructure are critical to safety. Incidents in 2023, such as the KMP Royce 1 fire and a crane accident, underscore the need for enhanced risk management and infrastructure maintenance. The study concludes that gaps in safety training and compliance with safety procedures are key challenges. Additionally, extreme weather and suboptimal heavy equipment conditions increase operational risks. Recommendations include improving safety management practices, enhancing infrastructure maintenance, and providing regular training for workers to strengthen operational safety at Merak Port.

Investigation of the statistical data on the technical condition of brake equipment components of passenger carriages in operation

In the article, a study was conducted on improving the efficiency of braking systems in railway transport. Based on the information obtained regarding the maintenance of passenger cars, an analysis of brake equipment component failures was performed. Key trends and factors determining brake equipment component failures were identified from the collected statistical data for the reporting period. The main brake equipment components with the highest number of failures were highlighted. It was established that a significant portion of transport incidents due to brake failures in passenger cars occurs during operation, negatively impacting traffic safety. The collected data on the failures of electro-pneumatic distributors in passenger cars were systematized using the specialized software package "STATISTIKA". The research established that seasonal factors influence the total number of failures. A diagnostic system for the braking equipment of passenger trains was proposed to ensure traffic safety. The diagnostic system allows for the construction and analysis of mathematical models that describe the operational state of the braking equipment of passenger rolling stock as a whole. The diagnostic system enhances the reliability of brake operation, monitors air pressure in the brake line, ensures the inter-repair periods of passenger car components, and reduces the time required to detect failures both en route and during train stops. Recommendations for diagnosing the braking system of passenger trains were developed for continuous inspection and timely identification of failures. Their application will enable the prediction of the lifespan of pneumatic and electro-pneumatic system components in passenger cars, ensure the inter-repair operational periods of cars based on their current technical condition, and enhance traffic safety in railway transport.

Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines

As the number of intelligent devices in mining environments increases, the transmission time for large datasets, including equipment status and environmental parameters, also rises. This increase leads to longer response times for service requests, making it difficult to meet the equipment’s real-time requirements. Edge computing effectively addresses the demands for low latency and high performance. However, the deployment of edge nodes can negatively affect overall service performance due to resource limitations and node heterogeneity. In this paper, we propose two node deployment strategies: an improved genetic algorithm (IBGA) for fixed device scenarios and an improved sand cat swarm optimization algorithm (ISCSO) for mobile device scenarios, both accounting for the mobility characteristics of the devices. Additionally, we developed a simulation platform based on a production line system and an intelligent patrol vehicle to evaluate the proposed method’s effectiveness. The experimental results show that the IBGA and ISCSO algorithms effectively reduce task delay and deployment cost. Both deployment methods outperform the benchmark algorithms and offer better service quality assurance.

Optimization of energy acquisition system in smart grid based on artificial intelligence and digital twin technology

In response to the low operating speed and poor stability of energy harvesting systems in smart grids, an energy harvesting optimization method based on improved convolutional neural networks and digital twin technology is proposed in the experiment. Firstly, a smart grid data transmission framework integrating digital twin technology is proposed. A digital twin mapping method based on time, data, and topology structure is used to realize the digital twin mapping at the device level of power grid. Through data synchronization and interaction between the physical power grid and the digital twin model, the operational efficiency and reliability of the power grid are improved. Then, the classical convolutional neural network and attention mechanism are used to comprehensively analyze the physical topology data in the smart grid energy acquisition system. The improved lightweight target detection model is combined to monitor the equipment status of the smart grid and extract key features. Simultaneously utilizing convolutional attention mechanism to dynamically adjust the feature weights of channels or spaces, completing the preprocessing of energy harvesting data. Finally, combined with energy harvesting and power grid switching system, the process of energy harvesting and power grid operation are optimized together. On the training and validation sets, when the channels exceeded 60, the proposed method achieved a system energy efficiency of 55% during operation. The system energy efficiency of the other three comparative algorithms was all less than 40%. In practical applications, as the energy transfer loss increased to 1.0, the system throughput increased to 50 bits. The electricity needs of different users were met, and the difference between power allocation and optimal power allocation was small, which was very reasonable. This proves that the research has effectively optimized the energy harvesting system in the smart grid, improving the efficiency and reliability of the system in practical applications of the smart grid. At the same time, in the increasingly severe energy problem, this system can further provide technical references for the utilization of renewable energy and help achieve the goal of sustainable energy.

Trend-constrained pairing based incremental transfer learning for remaining useful life prediction of bearings in wind turbines

Accurate remaining useful life prediction of bearings plays a significant role in safe operation of wind turbines. However, due to the harsh working condition, bearings often present complex and variable degradation trajectories, which leaving a challenge to guide the prediction by drawing on the historical experience with distribution differences, or even of cross equipment and cross conditions. To resolve this challenge, a novel trend-constrained incremental transfer prognosis (TCITP) method is proposed in this paper. Firstly, a feature fusion-based health indicator construction method is presented for a monotonic and smooth health indicator based on a novel monotonicity metric and a degradation increment cumulation algorithm. Secondly, a trend-constrained transfer learning method is designed for state estimation on the basis of a cross-domain transfer pairing algorithm. Thirdly, an incremental prognosis method is developed to update the prognostic model for remaining useful life prediction. Several bearings from accelerated degradation platforms and on-site wind turbine high-speed shafts are utilized to demonstrate the effectiveness and superiority of the proposed model. The error of the prediction is less than 3.5 samplings in last 10 samplings, and is also less than 5 samplings in last 20 samplings, which is able to provide advisable maintenance schedule to the on-site wind farm.

2D MOF‐Based Filtration‐Sensing Strategy for Trace Gas Sensing Under Intense F‐Gas Interference at Room Temperature

AbstractThe detection of trace impurity gases in fluorinated gas (F‐gas) that are widely used in the industry offers a significant avenue for equipment status monitoring and mitigating unnecessary emissions. However, the formidable electron affinity (EA) and adsorption propensity of F‐gas molecules render the identification of trace impurities within a high‐concentration F‐gas atmosphere exceptionally challenging. Herein, the filtration‐sensing strategy is proposed to realize highly sensitive and selective Room Temperature (RT) sensing of trace gases in the F‐gas environment. Through the innovative construction of a bilayer structure, comprising Co3(HITP)2 as the overlayer and SnO2 nanofibers (NFs) as the sensing layer, remarkably sensitive detection of trace impurity gases under intense F‐gas interference conditions is achieved. The efficacy of the Co3(HITP)2 overlayer is further corroborated through the incorporation of Pd‐SnO2 and MoS2‐SnO2 sensors, concurrently facilitating targeted quantitative identification within a complex gas mixture environment. The underlying sensing mechanism is predominantly attributed to interatomic adsorption interactions and the modulation of gas diffusion by microporous structures. This work provides pioneering insights into trace impurity detection within high‐concentration F‐gas atmosphere while presenting a potentially viable solution for the operational maintenance of F‐gas‐based industrial equipment (F‐equipment) in industrial applications.

Ranking of power grid facilities as a tool for choosing a repair management system

In the long-term planning of repairs at power grid facilities in conditions of limited operational and investment resources, it is important to determine the "points of maximum return", that is, the objects whose repair will cause the greatest effect in terms of the company's target priorities. This requires an operational assessment and ranking of electric grid facilities, taking into account the technological and resource characteristics of the energy company. The article presents a decision-making methodology that, based on operational calculations and priority assessment, enables to identify power grid facilities with the highest repair efficiency in the sense of criteria characterizing the strategic goals of asset management of an energy company. The methodology combines methods of modeling and calculating the probabilities of failure in consumer power supply systems (PSS) with the determination of indices of the technical condition of equipment, methods for calculating labor costs for maintaining and restoring reliability and assessing damage from its disruptions.The developed methodology allows the energy company — the owner of assets, when choosing the most effective repair management system according to enlarged indicators and simplified models, to form prioritization lists of objects for which the critical repair rank corresponds to its strategic priorities. The possibility of cost estimation of the total labor costs for the maintenance of facilities for each of the received prioritization lists in comparison with the repair fund stated by the energy company expands the scope of the methodology and increases the validity of the decisions made. The methodology has been verified and confirmed by consideration on the example of a real PSS of oil fields using up-to-date operational information.

Current signal analysis using SW-GAT networks for fault diagnosis of electromechanical drive systems under extreme data imbalance

Abstract In complex industrial environments, ensuring the safe operation and effective maintenance of electromechanical equipment is of paramount importance. Intelligent fault diagnosis based on deep learning is currently the most popular data-driven method. However, conventional intelligent fault diagnosis techniques face several challenges: (1) Most diagnostic models rely heavily on analyzing vibration signals. However, vibration sensors are difficult to deploy in space-constrained environments, and vibration signals are frequently contaminated by strong noise. (2) The prevalence of class imbalance between normal and fault data in equipment condition monitoring can lead to model over-reliance on information from a few classes. (3) Traditional diagnostic models presuppose data independence, neglecting the coupling relationships between data. To address the aforementioned issue, this paper proposes a Self-Weighted Graph Attention Networks (SW-GAT) based on motor stator current signal analysis, aimed at solving the fault diagnosis problem of critical transmission components in electromechanical systems under severely imbalanced data scenarios. Firstly, the raw current data is preprocessed using Stacked Auto-Encoders (SAE), and then the decoded current frequency-domain data is utilized to construct graphical data, thereby enhancing the non-common features and weak fault information in the current signals. Secondly, by introducing the graph pooling attention mechanism into GAT, the model can more effectively focus on useful fault feature information within the graph data. Finally, a novel interclass adjustment loss function is designed to adaptively adjust and balance class weights, enabling the model to pay greater attention to minority class samples and thereby improving the recognition accuracy for minority class faults. Validating the proposed method on two cases and comparing it with other advanced approaches, our method achieved the highest accuracy among the compared methods.

Methods of Balancing Technological Systems of Multiproduct Production

The functioning of the machine-building industry has its specifics, particularly periodic changes in the range (size, configuration, and others) of manufactured products. In addition, it is essential to consider the need to reduce the time spent on the production of each unit. Almost continuous changes in technology, failures in the supply of raw materials, uncoordinated logistics, and many other factors often cause significant and unproductive costs, leading to an increase in the technological stage. The most promising direction to reduce the technological time of manufacturing products by multiproduct enterprises is to reduce the waiting time owing to the uniform distribution of each technological transition according to the state of the available workshop equipment (plant, production area, enterprise). This study proposes a novel model of technological systems that enables the adaptation of technological processes for part manufacturing and comprises data structures that define their technical capabilities. The proposed algorithm facilitates a reduction in downtime and an increase in equipment utilization factor. It is possible to optimize the technological processes that change the structure of each production operation to adapt to the existing technology. Testing this methodology demonstrated a significant increase of 8% in the process utilization rate of machinery.

High-sensitivity and high-resolution triboelectric acoustic sensor for mechanical equipment monitoring

Traditional sensors for mechanical equipment monitoring often face challenges such as high cost, difficult installation and removal, and reliance on power sources. Herein, we introduced a polyvinylidene fluoride (PVDF) nanofiber membrane doped with BaTiO3 for constructing a triboelectric acoustic sensor (PB-TAS), innovatively designed for monitoring and recognizing the working conditions of mechanical equipment. This represents the first application of a triboelectric acoustic sensor for mechanical equipment monitoring. The PB-TAS demonstrates an ultra-broad frequency response range of 20–20,000 Hz, effectively covering the characteristic frequency spectrum (within 2000 Hz) during piston pump operation. It also exhibits extraordinarily sensitivity of 4.40 V Pa−1 at 85 dB, and ultra-high frequency resolution down to 0.1 Hz, enabling precise matching of the piston pump's acoustic characteristic frequencies. Integrated with deep learning techniques, the PB-TAS achieves real-time recognition of 15 distinct working conditions of a piston pump, with an impressive accuracy of 95.2 %. The PB-TAS, boasting exceptional sensitivity and frequency resolution, offers a cost-effective, miniaturized, self-powered, non-contact, and highly accurate solution for intelligent monitoring of hydraulic piston pumps.

Investigation of Abnormal Level Control Oscillations in a BWR Feedwater System

In the long-term operation of nuclear power plants, the aging of systems, structures, and components can lead to maintenance issues that must be dealt with to maintain cost-effective plant operations. One common issue affecting the currently operated boiling water reactors is the onset of unexpected level oscillations in feedwater heaters. This phenomenon can cause excessive cycling of drain valves and lead to premature failures. In this work, we develop a dynamic model of a set of feedwater heaters to determine the root cause of oscillations observed in an operating plant. Simulation results of various transient scenarios were used to investigate the effects of the controller parameters, boundary conditions, and possible valve and instrument issues. The analysis led to the conclusion that the most likely causes of the observed self-sustained oscillations in the system are the nonlinear behaviors of the drain valve and the level transmitter induced by degraded equipment condition. A partial plug of the pressure line used for level sensing in the system can account for a significant deadtime in the level transmitter, a nonlinear effect shown to induce self-sustained oscillatory behaviors.

Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN

Accurate identification of partial discharge (PD) and its types is essential for assessing the operating conditions of electrical equipment. To enhance PD pattern recognition under imbalanced and limited sample conditions, a method based on a Deep Autoencoder-embedded Generative Adversarial Network (DAE-GAN) is proposed. First, the Deep Autoencoder (DAE) is embedded within the Generative Adversarial Network (GAN) to improve the realism of generated samples. Then, complementary PD data samples are introduced during GAN training to address the issue of limited sample size. Lastly, the model’s discriminator is fine-tuned with augmented and balanced training data to enable PD pattern recognition. The DAE-GAN method is used to augment data and recognize patterns in experimental PD signals. The results demonstrate that, under imbalanced and small sample conditions, DAE-GAN generates more authentic PD samples with improved probability distribution fitting compared to other algorithms, leading to varying levels of enhancement in pattern recognition accuracy.

Исследование влияния отклонений параметров моделей синхронных генераторов на результаты расчета динамической устойчивости электроэнергетической системы

One of the important tasks solved in planning the electric power system (EPS) operation is the assessment of its transient stability. Use of incorrect parameters for EPS element models, in particular synchronous generators (SG), contributes to the occurrence of errors when determining the operating conditions of power systems, and, therefore, can lead to a decrease in the reliability of EPS operation. This article is devoted to the study of the impact of errors in SG model parameters on the transient stability assessment results. The analysis presented in this article allows us to substantiate the relevance of the SG parameter estimation task using a “passive experiment”, such as synchronized phasor measurements. This research is based on the theory of electrical circuits, mathematical description of electrical machines and transients in EPS, as well as statistical analysis. Computational experiments have been carried out in the software package MATLAB using the simulation modeling environment “Simulink”. During the research, two different multi-machine EPS models have been used, including the well-known “IEEE 9-bus” system. To quantitatively assess the influence of SG parameter uncertainties on transient stability analysis, the values of the maximum permissible fault clearance time are determined, as well as the values of the maximum power output of generating equipment in prefault conditions. The authors have carried out a comprehensive analysis of the influence of deviations in SG model parameters on the maximum fault clearance time, as well as on the maximum power output of generating equipment in the prefault conditions for a given fault duration. During the study, two approaches have been considered; in each case, the EPS transient stability assessment has been performed almost automatically, and quantitative indicators have been computed for the influence of SG model parameter deviations on the outcome of EPS transient stability analysis. Analysis of the computational experiment results has demonstrated a significant influence of the SG model errors on the errors in calculating certain transient stability-related parameters. For the considered scenarios, the spread of errors in calculating the maximum fault clearance time reached 100 % relative to a base-case (errorless) result, and the error in calculating the maximum power output of generating equipment in prefault conditions for a given fault duration may reach 20 % relative to the errorless result.

РАЗРАБОТКА ТЕХНОЛОГИЧЕСКОЙ СХЕМЫ И ОБОСНОВАНИЕ ПАРАМЕТРОВ ОЧИСТКИ СЕМЯН ЛЮЦЕРНЫ

Despite the high effectiveness of chemical preparations against weeds, this fight begins with the preparation of seed material on seed cleaning lines. An urgent problem in cleaning crop seeds is the isolation of dodder seeds in the preparation of alfalfa seeds. Alfalfa cleaning is a time-consuming process in itself, due to the complex structure of the combine heap. In this regard, it is important to develop a technological scheme for cleaning the alfalfa heap, which will allow you to effectively separate dodder seeds on a par with other impurities. When developing the technological scheme for alfalfa purification, the parameters of the initial heap and its components were determined. When determining the composition of the alfalfa heap, it was found that the alfalfa heap consists of 40-60% of organic impurities, 10% of puny seeds, and up to 10% of the heap consists of unthreshed beans, and the alfalfa seeds themselves in the initial heap are usually no more than 30% of the total weight, it was also determined that weed seeds make up less than 1% of the original weight, nevertheless, the heap contained 0.02% of dodder seeds. On laboratory screens with slotted notches and round holes, the dimensional characteristics of the components are determined. On the sail classifier, the distributions of the components according to the critical speed of vinow were established. The data obtained made it possible to develop a technological scheme for cleaning alfalfa seeds, which allows you to effectively remove dodder seeds with minimal losses. Using the same equipment in laboratory conditions, the proposed alfalfa cleaning scheme was tested, which showed that by combining aspiration and sieve cleaning, it is possible to achieve 99.45% purity of seeds, with losses of no more than 1%, dodder was not found in the final material.

Negative impact of vibration on process pipelines of a compressor station with electrically driven gas pumping units

Relevance. In gas industry, the amount of equipment that has outlived its intended service life is increasing every year. In accordance with the law “On Industrial Safety of Hazardous Production Facilities,” compressor stations are dangerous objects, the reliable operation of which largely determines the condition of main gas pipelines. Technological pipelines of a compressor station are important objects of this system. They are subject to strict requirements due to the action of static and dynamic loads on them. Reliability of compressor stations is based on the results of a study of the condition of the object and the main factors affecting increased wear of equipment. The solution to wear problem is timely monitoring of equipment, in particular level measurement and determining the causes of pipeline vibrations using vibration diagnostics methods, and frequency and modal analysis methods. Aim. To study the causes of increased dynamic loads on the technological piping that arise during the operation of electric and gas pumping units at a compressor station using vibration monitoring methods. Methods. Vibration monitoring methods to analyze the causes of increased vibration values in the process piping of a compressor station with an electric gas pumping unit. Results and conclusions. The authors have measured vibration in a wide frequency band and assessed the technological condition of the main equipment of the compressor station with EGPA-6.3/8200-56/1.44-R based on the regulatory documentation STO Gazprom 2-2.3-324-2009. The main frequencies of the root-mean-square value of the vibration velocity, at which the highest vibration values were observed, were identified, and a modal analysis was performed using ANSYS software. Based on the results of natural studies and modal analysis, it was concluded about the occurrence of resonant high-frequency oscillations, multiple of the rotor rotation frequency.

An Extended Method Based on the Geometric Position of Salient Image Features: Solving the Dataset Imbalance Problem in Greenhouse Tomato Growing Scenarios

Machine vision has significant advantages in a wide range of agricultural applications; however, acquiring a large number of high-quality image resources is often challenging in actual agricultural production due to environmental and equipment conditions. Therefore, crop image augmentation techniques are particularly important in crop growth analysis. In this paper, greenhouse tomato plants were used as research subjects to collect images of their different fertility stages with flowers and fruits. Due to the different durations of each fertility period, there is a significant difference in the number of images collected. For this reason, this paper proposes a method for balanced amplification of significant feature information in images based on geometric position. Through the geometric position information of the target in the image, different segmentation strategies are used to process the image and supervised and unsupervised methods are applied to perform balanced augmentation of the image, which is combined with the YOLOv7 algorithm to verify the augmentation effect. In terms of the image dataset, the mixed image dataset (Mix) is supplemented with mobile phone images on top of in situ monitoring images, with precision increased from 70.33% to 82.81% and recall increased from 69.15% to 81.25%. In terms of image augmentation, after supervised balanced amplification, the detection accuracy is improved from 70.33% to 77.29%, which is suitable for supervised balanced amplification. For the mobile phone dataset (MP), after amplification, it was found that better results could be achieved without any amplification method. The detection accuracy of the mixed dataset with different data sources matching the appropriate amplification method increased slightly from 82.81% to 83.59%, and accurate detection could be achieved when the target was shaded by the plant, and in different environments and light conditions.