Machine Time Research Articles

SOLUTION OF PHYSICALLY NONLINEAR PROBLEMS OF DEFORMATION OF MASSIVE AND THIN-WALLED PRISMATIC BODIES

The results of a numerical study on test examples of the convergence of the developed version of the semi-analytical method of finite elementsare presented. A comparison of machine time and accuracy of solving elastic and plastic problems obtained on the basis of finite elements with variableand averaged mechanical parameters was performed.The convergence of the finite element method and the semianalytical finite element method in the calculation of prismatic bodies loaded with localized and distributed external influences was studied. An effective numerical approach to the study of arbitrarily loaded massive and thin-walled prismatic bodies of complex shape, the deformation of which can pass beyond theelastic limit of the material, is being developed based on the finite element moment scheme and the semianalytical variant of the finite element method. Due to the representation of displacements by polynomials and the use of iterative methods of solving systems of equations, this approach is developed in relation to the calculation of objects with arbitrary boundary conditions on the ends, which made it possible to expand the area of effective application of the semi-analytical method of finite elements to a new class ofproblems.A number of new complex problems of elastic and elastic-plastic deformation of massive and thinwalled prismatic bodies, which have an independentapplied value, have been solved.

Energy consumption in MRI: Determinants and management options

BackgroundEnergy consumption awareness is a known concern, and radiology departments have energy-intensive consuming machines. The means of energy consumption management in MRI scanners have yet to be evaluated. PurposeTo measure the MRI energy consumption and to evaluate the means to reduce it. Materials and methodsData was retrieved for two MRI scanners through the hospital's automated energy consumption measurement software. After correlation with picture archiving and communication system (PACS) files, they were segmented by machine and mode (as follows: stand-by, idle and active) and analyzed. Active mode data for a specific brain MRI protocol have been isolated, and equivalent low energy consuming protocol was made. Both were performed on phantom and compared. Same protocol was performed on a phantom using 3.0T 16 and 32 head channels coils. Multiples sequences were acquired on phantom to evaluate power consumption. ResultsStand-by mode accounted for 60 % of machine time and 40 % of energy consumption, active mode accounted for 20 % machine time and 40 % energy consumption, idle mode for 20 % imachine time and 20 % consumption. DWI and TOF sequences were the most consuming in our brain-MRI protocol. The low energy consuming protocol allowed a saving of approximately 10 % of energy consumption, which amounted for 0.20€ for each examination. This difference was mainly due to an energy consumption reduction of the DWI sequence. There were no difference in consumption between a 3.0T 16 and 32 channels head coils. Sequence's active power and duration (especially considering slice thickness) have to be taken into account when trying to optimize energy consumption. ConclusionThere are two key factors to consider when trying to reduce MRI scan energy consumption. Stand-by mode energy consumption has to be taken into account when choosing an MRI scan, as it can't be changed further on. Active mode energy consumption is dependent of the MRI protocols used, and can be reduced with sequences adaptation, which must take into account sequence's active power and duration, on top of image quality.

Predicting Tencent's Stock Price: A Comparative Analysis of Machine Learning Algorithms

Chinese internet companies, such as Tencent, are bringing a new energy to the global market. As one of the largest internet companies in China, Tencent's stock price fluctuations hold significant importance for investors and the market. Accurate forecasting of stock prices is of great importance to make high-yield decisions and manage risk. This paper aims to provide investors with effective and scientific references for decision-making by analyzing various forecasting methods. The dataset China-Techgiant-Stock-Data-in-HK-Market-2022 from Kaggle is used in this paper. This study utilizes various machine learning and time series analysis methods, including linear regression, SVM, random forest, LSTM neural network, and ARIMA, to predict stock prices using historical data. The models' accuracy and performance are compared, with traditional machine learning methods (linear regression, SVM, and random forest) contrasted against deep learning and time series analysis methods (LSTM neural network and ARIMA). It turns out that LSTM has the best prediction results. This can better guide investors in their stock decisions.

A modified Aquila optimizer algorithm for optimization energy-efficient no-idle permutation flow shop scheduling problem

Increasing energy consumption has faced challenges and pressures for modern manufacturing operations. The production sector accounts for half of the world's total energy consumption. Reducing idle machine time by em­ploying No-Idle Permutation Flow Shop Scheduling (NIPFSP) is one of the best decisions for reducing energy consumption. This article modifies one of the energy consumption-solving algorithms, the Aquila Optimizer (AO) algo­rithm. This research contributes by 1) proposing novel AO procedures for solving energy consumption problems with NIPFSP and 2) expanding the literature on metaheuristic algorithms that can solve energy consumption problems with NIPFSP. To analyze whether the AO algorithm is optimal, we compared by using the Grey Wolf Optimizer (GWO) algorithm. It com­pares these two algorithms to tackle the problem of energy consumption by testing four distinct problems. Comparison of the AO and GWO algorithm is thirty times for each case for each population and iteration. The outcome of comparing the two algorithms is using a t-test on independent samples and ECR. In all case studies, the results demonstrate that the AO algorithm has a lower energy consumption value than GWO. The AO algorithm is there­fore recommended for minimizing energy consumption because it can produce more optimal results than the comparison algorithm.

In-cabin and outdoor environmental monitoring in vehicular scenarios with distributed computing

Accurate environmental monitoring is becoming the basis for assuring sustainable development in administrations at different levels, including cities and industry as key actors. However, current techniques rely on static stations that may not be representative of larger areas, for the case of outdoor scenarios, or even not considering indoor spaces where people can remain for long periods. This is the case of vehicles. The COVID-19 pandemic has remarked the importance of measuring air quality indoors, for instance. With the aim of solving this two-fold issue, this work proposes an in-cabin and outdoor air pollution monitoring system to assure healthy conditions when travelling, driving and operating vehicles, and to analyse the evolution of environmental parameters in cities. This effort is carried out exploiting distributed computing with micro-services, betting for an on-board hardware solution provided with sensors for measuring particulate matter, CO, CO2, NO2, O3, temperature and humidity. While basic data pre-processing is carried out in this acquisition unit, edge processing is performed on a single board computer aboard and intermediary communication nodes in the network path from the vehicle to the cloud. Vehicle connectivity is provided by 4G cellular and Low-Power Wide-Area (LPWAN) networks. Global environmental perception is acquired by cloud-based software powered by machine learning and time series analysis. The whole solution has been validated and tested in the city of Cartagena (Spain), with good performance in terms of data collection, communication links and service offered.

Applying laser induced breakdown spectroscopy (LIBS) and elemental imaging on marine shells for archaeological and environmental research.

Using LIBS for the analysis of archaeological and geological marine mollusc shells is a growing research area that relies on customised instrumentation and specific workflows that can accommodate the variety and precision of the required sampling parameters. However, the increased efficiency offered by LIBS, which enables the study of a larger quantity of shell samples for temperature variation, ecological parameters, and human consumption practices, outweighs the initial efforts required to develop customised instrumentation and workflows. In this work, we present detailed specifications and parameters for the development of a LIBS system capable of generating Mg/Ca images on marine shells that directly correlate with seasonal sea temperatures. Our main objective was to develop specifications that enable easy adaptation of LIBS systems to existing laboratories for studying hard-tissue samples. These specifications were used to develop a customised micro-LIBS system and apply it to a real-world example of an archaeological study to better understand its efficiency on the marine mollusc shells and demonstrate its potential for broader applications in interdisciplinary research. In total 101 shell specimens have been analysed within a time frame of approximately 71h of machine time, producing 234 images (100µm resolution: 100 images, 30µm resolution: 134 images). SEM analysis of the irradiated sections of the shell revealed a primary ablated area of 10-15µm in diameter, while a secondary affected area of the shell's crystal fabric extended to 30-50µm after repeated shots. Overall, this new customised system reliably and efficiently analysed marine mollusc specimens without major destructive effects, enabling additional analyses for other proxies to be carried out. This study highlights the potential of the LIBS method for interdisciplinary research, encompassing applications in paleoclimatology, marine ecology, and archaeology.

Utilization of Johnson's Algorithm for Enhancing Scheduling Efficiency and Identifying the Best Operation Sequence: An Illustrative Scenario

This study's primary objective is the enhancement of makespan optimization, thereby minimizing unproductive time for both machinery and tasks. The research employs a case study methodology, focusing on job scheduling within anElectronicsmanufacturing facility, with a particular emphasis on resource availability. It implements Johnson's algorithm and its expanded versions designed for both two-machine and three-machine scenarios within the context of flow shop scheduling. The principal aim is to identify optimal sequences for scheduling. Within this framework, the investigation computes idle time and makespan metrics for individual machines, utilizing task processing durations and in-out timestamps. The findings reveal anoptimal idle time of 6.21 minutes and a makespan of 142.06minutes for scenarios involving two machines. Furthermore, the research extends its analysis to scenarios with three machines, where two machines are combined in each group, resulting in an optimal idle time of 5.22minutes for combined machine A2, 14.98minutes for combined machine A3, and a makespan of 192minutes. This study provides valuable insights applicable to industries dealing with diverse machinery and components, ultimately contributing to improved scheduling and productivity.

Evaluation of Cardiac Substructures Dose Sparing in Single and Dual Isocenter RapidArc™ Radiotherapy Planning for Synchronous Bilateral Breast Cancer.

Purpose This study compares the dosimetry and dose sparing of cardiac substructures in single isocenter and dual isocenter RapidArc™ (Varian Medical Systems, Palo Alto, California, United States) radiotherapy planning for synchronous bilateral breast cancer. Methodology Six synchronous bilateral breast cancer (SBBC) patients received adjuvant radiation with the prescribed dose of 40.05 Gy in 15 fractions to the planning target volume (PTV) without local lymph nodal regions. PTVs and organs at risk (OARs), including both lungs, esophagus, spinal cord, heart, and left anterior descending coronary artery (LAD), both atria and ventricles were contoured. Single isocentric RapidArc (SIRA) and dual isocentric RapidArc (DIRA) plans were made for each patient and dosimetric differences between these two techniques were evaluated. Results There was no statistically significant difference in conformity index (CI) values between SIRA and DIRA plans, with 0.9681±0.01and 0.9721±0.01 (p=0.505), respectively. SIRA planning showed superior homogeneity with homogeneity Index (HI) values of 0.0999±0.01 compared to DIRA planning with HIvalues of 0.1640±0.12 (p=0.230). The mean LAD dose of SIRA was valued higher than that of DIRA planning. Lower mean doses were obtained for both lungs in SIRA plans compared to DIRA plans. Meanwhile, doses to the right atrium, left atrium, left ventricle, right ventricle, and esophagus showed no statistical significance between these two techniques, except in the spinal cord. Conclusion Both SIRA and DIRA plans have satisfactory outcomes in sparing OARs. Meanwhile, SIRA techniques have less setup time and overall machine time.

Forecasting performance of machine learning, time series, and hybrid methods for low‐ and high‐frequency time series

One of the main objectives of the time series analysis is forecasting, so both Machine Learning methods and statistical methods have been proposed in the literature. In this study, we compare the forecasting performance of some of these approaches. In addition to traditional forecasting methods, which are the Naive and Seasonal Naive Methods, S/ARIMA, Exponential Smoothing, TBATS, Bayesian Exponential Smoothing Models with Trend Modifications and STL Decomposition, the forecasts are also obtained using seven different machine learning methods, which are Random Forest, Support Vector Regression, XGBoosting, BNN, RNN, LSTM, and FFNN, and the hybridization of both statistical time series and machine learning methods. The data set is selected proportionally from various time domains in M4 Competition data set. Thereby, we aim to create a forecasting guide by considering different preprocessing approaches, methods, and data sets having various time domains. After the experiment, the performance and impact of all methods are discussed. Therefore, most of the best models are mainly selected from machine learning methods for forecasting. Moreover, the forecasting performance of the model is affected by both the time frequency and forecast horizon. Lastly, the study suggests that the hybrid approach is not always the best model for forecasting. Hence, this study provides guidelines to understand which method will perform better at different time series frequencies.

The Impact of Vibrating Screen Startup Time on Vibration Amplitude and Energy Consumption in Transient State

The article presents the results of research on the trajectory and amplitude values during the startup of a vibrating screen, depending on the set startup time. The research was conducted using a suspended vibrating screen with a segmental vibration trajectory, where vibration excitation was achieved through two vibratory motors directly attached to the screen frame. The vibratory motors were powered with three-phase voltage and connected to the grid through frequency converters, allowing for current control in steady-state and startup conditions. The analysis focused on the vibration trajectory and the resulting amplitude values for nine selected startup times (ranging from 0 s to 8 s). Two measurement methods were employed: using piezoelectric acceleration sensors and optical recording. Additionally, the momentary power consumption by the screen drive was recorded, and the maximum current flowing in the power supply circuit was determined. These studies enabled the determination of the most favourable startup parameters that reduce the power demand of the drive without significantly affecting the kinematic parameters of the screen during its startup. The results of experimental research presented in the article regarding the startup time of the screening machine and its influence on vibration parameters have shown that the startup time significantly impacts the amplitude values that occur during startup. In the analysed case, the most favourable startup time was 3 s. Power consumption studies during startup revealed that startup time has a notable influence on momentary power consumption. The conducted research demonstrated that for startup times below 0.2 s, the maximum momentary power was the highest (reaching 5 kW, with a current consumption of 4.5 times the rated current). Regarding energy efficiency, the most favourable startup times exceed 2.0 s. Extending these startup times can significantly reduce the power demand of the drive, even up to threefold, thereby enabling the selection of motors with much lower power ratings.

Joining of hybrid busbars for E-Mobility: An economic and environmental study

This paper presents a model to evaluate and analyze the costs of joining hybrid (copper-aluminum) busbars when different production processes are deployed. The process-based cost model (PBCM) is built upon the subdivision of the production cycle in three different stages related with the fabrication or purchase of auxiliary joining elements, preparation of the individual copper and aluminum conductors, and final joining of the hybrid busbars. The total cost per hybrid busbar is obtained by converting the major physical, human, and financial resources associated with the production cycle into itemized costs that make use of the expenses in materials, labor working hours, number and usage time of machines and tools, among other production costs. Application of the PBCM is illustrated with three different joining processes and enriched with a life cycle assessment (LCA) focused on the environmental performance of hybrid busbars throughout its fabrication, service use and end of life. The combined economic and environmental sustainability analysis of joining hybrid busbars allows concluding that despite conventional fastening being the cheaper process it has the highest environmental impact due to the use of bolts, nuts and washers made from galvanized medium carbon steel. Injection lap riveting arises to be the most well-balanced process in terms of production cost and environmental impact.

Genetic algorithm with a Bayesian approach for multiple change-point detection in time series of counting exceedances for specific thresholds

Although the applications of Non-Homogeneous Poisson Processes (NHPP) to model and study the threshold overshoots of interest in different time series of measurements have proven to provide good results, they needed to be complemented with an efficient and automatic diagnostic technique to establish the location of the change-points, which, when taken into account, make the estimated model fit poorly in regards of the information contained in the real one. Because of this, a new method is proposed to solve the segmentation uncertainty of the time series of measurements, where the generating distribution of exceedances of a specific threshold is the focus of investigation. One of the great contributions of the present algorithm is that all the days that trespassed are candidates to be a change-point, so all the possible configurations of overflow days under the heuristics of a genetic algorithm are the possible chromosomes, which will unite to produce new solutions. Also, such methods will be guarantee to non-local and the best possible one solution, reducing wasted machine time evaluating the least likely chromosomes to be a feasible solution. The analytical evaluation technique will be by means of the Minimum Description Length (MDL) as the objective function, which is the joint posterior distribution function of the parameters of the NHPP of each regime and the change-points that determines them and which account as well for the influence of the presence of said times. Thus, one of the practical implications of the present work comes in terms of overcoming the need of modeling the time series of measurements, where the distributions of exceedances of certain thresholds, or where the counting of certain events involving abrupt changes, is the main focus with applications in phenomena such as climate change, information security and epidemiology, to name a few.

Comparison and Breakdown of Cost in Head and Neck Cancer Radiation Therapy and Adaptive Replanning

Head and neck cancers pose a significant challenge to radiation oncologists due to several factors, including rapid shrinkage during radiation and patient weight loss. As the target and patient anatomy change, re-simulation and re-planning are often necessary, which precipitates additional time, resources, and cost. However, the true added cost of providing adaptive replanning for head and neck cancer patients receiving radiation therapy (RT) is often unknown. We hypothesize that adaptive replanning adds significant cost to treatment, primarily driven by the additional personnel time in the replanning process. The total costs of providing standard radiation therapy and treatment with adaptive replanning for head and neck cancer were calculated using time-driven activity-based costing. We created process maps for each step from consultation to end of treatment, utilizing data that included the space, personnel involved, equipment and supplies used, and time spent. Capacity cost rates were calculated for all resources using a combination of publicly accessible cost data, financial data from our institution, and time averages for each activity obtained from personnel interviews and clinical measurements. We included the following domains in our process maps: consultation, CT simulation, treatment planning, new treatment start, daily beam treatments, weekly on-treatment visits, CT Re-simulation, treatment replanning, and new start for replanned treatment. We found that the total cost for providing standard therapy was $12,216.39 US Dollars (USD), while the total cost of adaptive replanning was $14,705.14 USD, for a relative increase in cost of 20.4%. The most expensive factor for both treatment arms was the cost of daily treatments on the linear accelerator, representing 66.1% and 53.3% of the standard and adaptive replanning, respectively. The second largest driver of cost for both was weekly on-treatment visits with a physician, representing 11.37% and 9.45% of the total cost, respectively. Of the total cost difference between standard therapy and adaptive replanning, 64% was due to personnel costs, 27.9% was due to space/equipment costs, and 8.1% was attributable to material costs. We found that adaptive replanning increased the overall cost of radiotherapy for head and neck cancers by a significant amount. The additional personnel time with resim and replanning, as well as machine time on the CT simulator are the primary reasons for this increase. In addition, use of these resources represents an opportunity cost in the clinic. Opportunities to make adaptive replanning more cost-effective include incorporation of AI tools for volume delineation and/or treatment planning to reduce personnel time, as well as using daily cone beam CT images instead of a new CT simulation, when feasible.

Development of a monitoring system for technological equipment of a meat processing plant

Relevance. In the production of meat products and semi-finished products, equipment from the 1990s is often used. Such equipment is not equipped with modern means of transmitting diagnostic data and information about the current status via wireless communication, and requires periodic monitoring during the work shift. Lack of timely diagnostic data can lead to equipment failures and unexpected downtime. At the same time, the development of technical means of communication makes it possible to connect industrial equipment to the Internet and remotely receive data on the current state of the equipment online.Methods. The objects of the study were the MULTIVAC R5200 vacuum thermoforming packaging machine and thermal chambers with installed controllers Aditec MIC 2410, Aditec TP 1000, MIKSTER MCC 2100. Methods of object-oriented programming in Python 3.4.4 were used for the monitoring system under study. For network interaction of technological equipment with cloud services, the MQTT-v3.1.1 data transfer protocol was used.Results. The use of a monitoring system based on the industrial Internet of Things platform made it possible: when packing boiled and smoked sausages, to reduce the maintenance time of the packaging machine and the consumption of spare parts, to reduce the production of low-quality products; when carrying out technological processes in thermal chambers, to completely eliminate spoilage of semi-finished products, to improve the quality of the finished product. In the future, the spread of such technology to the equipment of the entire enterprise will make it possible to solve the problems of collecting a large amount of data on the current state of the equipment, conducting analysis and making managerial decisions.

A tri-individual iterated greedy algorithm for the distributed hybrid flow shop with blocking

Scheduling problem with blocking is an attractive research direction due to its wide practical applications. However, in the distributed hybrid flow shop problem (DHFSP), there are few studies on blocking constraints. In this article, we study a distributed hybrid flow shop scheduling problem with blocking constraints (DBHFSP), where the goal is to minimize the maximum completion time, and propose a tri-individual iterated greedy (TIG) algorithm based on the characteristics of the DBHFSP. First, an active decoding strategy is designed to reduce the idle time of machines. Afterward, a framework of multiple iterative solutions is designed to enhance the diversity of the solutions, in which the multiple solutions are generated through a neighborhood search. Then, a heuristic rule based on blocking constraint is proposed to generate promising initial solutions. In addition, an insertion-based search strategy based on rotating critical factories is designed to accelerate the convergence speed of the TIG algorithm. Finally, the proposed algorithm is compared with state-of-the-art algorithms and classical intelligent optimization algorithms. The experimental results show that the TIG algorithm is superior to the comparison algorithm in solving the DBHFSP.

Measurement of Occupational Eye and Thyroid Radiation Doses in Pediatric Interventional Cardiologists at a Tertiary Hospital.

Background Advances in imaging techniques have led to increased utilization of imaging devices in catheterization laboratories. Invasive surgical procedures for cardiac disorders have been largely replaced by fluoroscopic cardiac catheterization. With this increase, concerns and risks associated with exposure to ionizing radiation among interventional cardiologists are growing. This study aims to measure and compare the occupational doses to the eye lens and thyroid of pediatric interventional cardiologists during different procedures in the catheterization laboratory and its significance. Methodology In this study, cardiologists wore bandanas with attached dosimeters to measure the absorbed doses to the eyes and thyroid gland. The dosimeters were collected for reading. The procedure types were also collected. In addition, the total fluoroscopy time and tube voltage of the biplane machine were measured. SPSS version 23 (IBM Corp., Armonk, NY, USA) was used to analyze the data. The characteristics of the study sample were described using simple counts and percentages, whereas means and standard deviations were used for continuous variables.Statistical significance was set at p-values <0.05. Results A total of 93 procedures were evaluated. The mean absorbed doses for all 93 procedures in both eyes and thyroid were 0.09 mGy and 0.08 mGy, respectively. A significant difference was found between the left and right eye measurements (p = 0.034), with higher doses administered to the left eye. However, no significant difference was observed between the right and left thyroid doses (p = 0.281). Significant correlations were found between the eye and thyroid doses and the procedure type (p = 0.02 and p = 0.009, respectively). Conclusions A significant amount of radiation was measured in the measurements of both organs. In addition, radiation dose measurements varied between different types of procedures. Our current results indicate the importance and necessity of applying the radiation protection concept of dose optimization.

Optimum Average of High-Speed to Describe of Aircraft by the Flying-Tests

In determinate models of control dynamic objects usage minimum of information operating physical factors. Criterions orthogonal tests of optimization should be damage enter probable (density of probability), or another realization of vector accidental indignation and application put into practice, example object of control type aircraft. Example variable (unsteady) control of factor aircraft (realization accidental of parameters) should be guarantee execution concrete of realization purposes, even in maximum be in difficulties. This demand include in number criterions of optimization achievement extreme of expectation value of quality functional of object of control. Application flight fitness probability achievement execution completion of mission. Definition irregularity control of decision with take into consideration should be name problem optimum mean quantity or statistical optimization of dynamic system. By the solution statistical problem optimization arise news enough session difficulties. She connected with decision theoretical questions, which now should be by the analysis of determinate models and organization of quality procedure. Latter procedure realization by the considerable increase machine time and including methods statistical simulation (artificial reproduction) of property object of control (aircraft). Application criterion statistical optimization propose, what beforehand (in good time) statistical according to official returns. Receipt of statistical information realization analysis of experience developmental. Methods optimization propose arbitrary law of random characteristic. Reliable of conclusions under certain conditions possess statistical characteristic.

Emulation of IEEE STD 421.5/Industrial Excitation Systems Using a Micro-Alternator's Exciter

Micro-alternators, having similar parameters as turbo-alternators, play a significant role in the experimental evaluation of power system dynamics. Early thyristor excitation systems of micro-alternators have limited re-configurable options. Three possible control structures, two closed-loop control structures proposed in the literature and one open-loop control structure proposed in this paper, including a time constant regulator to mimic large machine time constants, are evaluated to emulate any IEEE 421.5 standard excitation system model or interface an industrial excitation system controller hardware in the loop (CHIL). It is shown that the communication delay introduced in CHIL does not impact the closed-loop stability within the bandwidth requirements of the excitation systems. One of the closed-loop structures, which is more generalized, and the open-loop structure performances are compared for emulating IEEE AC4C, DC1C, and ST1C excitation systems on a laboratory micro-machine based single machine infinite bus test system with buck exciter and H-bridge chopper (HBC) exciter using EMTP simulations under small and large disturbances. HBC exciter is used in the experimental validation with both the closed-loop and the open-loop control structures. The proposed open-loop structure is easy to implement and performs better than the closed-loop structure.

Assessment of the Influence of Internal Factors on the Indicators of Passenger Elevator Units Utilization Based on the Results of Regular Monitoring

Introduction. Ensuring high reliability and safety of operation of passenger elevator units is largely determined by the implemented maintenance conditions (MC). The frequency of performing preventive actions depends, first of all, on the level of elevator utilization. Time, power indicators and the degree of remaining life are used to evaluate it. Among the time indicators, the net machine time coefficient (NMT) and the turn-on frequency are accepted, which are random variables depending on a number of internal factors characterizing the operating conditions of the unit. The work objective is to establish the relationship between the average values of NMT, as one of the main indicators of the load of the elevator unit, and the main internal factors.Materials and Methods. The research was carried out on the basis of processing and generalization of statistical materials of dispatching control of time indicators of a number of passenger elevator units. 11 elevators were randomly selected, differing in the number of floors, the specific number of residents using the elevator, and the speed of movement of the cab. Graphical-analytic methods were used to construct empirical dependences of NMT on the number of residents, the speed of the cab and the number of floors of the building. Along with the technical parameters of the elevator, random changes in the NMT indicators for individual periods of the day were taken into account.Results. Empirical dependences of the NMT on the main internal factors — the density of occupation, the number of floors of the building and the speed of the cab movement were established. Mathematical models provided results adequate to experimental values. The error when comparing the calculated data with the actual data did not exceed 10 % in most cases.Discussion and Conclusion. The value of the empirical dependencies obtained consists in the ability to assess the workload of units during the current period of operation without additional multi-day measurements. Empirical formulas can be used as basic relations in simulation modeling at any stage of the life cycle.

Research on Fault Prevention and Maintenance System of Automatic Substation Primary Equipment Based on Decision Tree Algorithm

Abstract Electric power enterprises are developing rapidly in the era of big data information digitization. At this stage, the total number of substations is gradually increasing, the structure of the power engineering system is slowly becoming complicated, and the video monitoring system instantly collects a lot and contains a lot of noisy data information, which affects the power supply system’s access to effective data information and fault detection. To prevent the above phenomenon. This paper selects a decision tree algorithm to obtain and analyze meaningful operation-confirming information from a large amount of data information, and then can quickly and confirm the diagnosis of common fault machines and equipment in substations, reduce the running time of common fault machines, and improve the safety and reliability of primary equipment in substations with automation technology. The paper describes the basic concept of big data mining common algorithm and its data mining algorithm in the automation technology substation primary equipment fault detection, selected the typical alarm signal to start the analysis, and categorization and collocation solution. A decision tree algorithm entity model is built, several classical decision tree algorithms are described, and their data analysis is carried out for each attribute, and then the decision tree algorithm is improved. According to build the decision tree algorithm according to improve the decision tree algorithm under the fuzzy set base theory, mainly by expertise in the four on cannot identify the association, rough set and up close and down close, similar and membership relationship, expertise concise to optimize the calculation method. And the common ID3, C4.5, and CRAT algorithm of each property is compared and analyzed, and the results show that: compared with C4.5 and ID3, the boosted optimization algorithm has higher classification accuracy and can model rate more quickly. The research in this paper can quickly diagnose the automation technology substation primary equipment and fault phenomena, and its establishment of the whole process is easy, the scope of application is relatively high, and it has wide applicability.