Rate Increase Factor Research Articles

Artifical Neural Network for Forecasting Electricity Consumption in Energy Enterprises

The concept of "Digital Transformation 2030", which defines the national goals and strategic objectives of the development of the Russian Federation for the period up to 2030, specifies specialized goals and objectives. These goals and objectives are an important message for the introduction of intelligent information management systems based on digital technologies in the electric power industry in general and energy consumption in particular. The main challenges for the transition to digital transformation are: increasing the rate of tariff growth for the end consumer; increasing deterioration of the network infrastructure; the presence of excessive network construction; increasing requirements for the quality of energy consumption. Based on the above, this article is devoted to the development of a method for the formation of an intelligent con­trol system at an energy enterprise by obtaining an early forecast of the amount of electricity required. The predicted values will help not only to increase the energy efficiency of the company through the implementation of specialized energy saving measures, but also to reduce the financial costs of electricity. The solution to this problem is presented in the form of an intelligent neural network system. The main advantages of this artificial neural network are versatility, high-speed and accurate learning, as well as a small amount of training data. The artificial neural network itself is based on the freely distributed TensorFlow machine learning software library, and a modified error backpropagation method is used as training, the main difference of which is the addition of an artificial neural network learning rate increase factor. The results of the analysis of the effectiveness of the method showed that the proposed intelligent neural network system is more accurate (including relative to similar solutions): the average error does not exceed 3.08 % over the entire time of the experiment. This will allow companies to carry out energy saving measures, which will be especially useful in the current economic realities.

A system based on an artificial neural network of the second generation for decision support in especially significant situations

Currently, specialized system models implemented on the basis of decision support in exceptional (emergency) situations (states) using machine learning, artificial intelligence (including using neural networks) to reproduce, predict and prevent (or minimize the risk of consequences) in exceptional situations are useful and are becoming increasingly popular. Floods also fall under such exceptional situations and states, on the basis of which the task of early forecasting of an exceptional situation arises, using the example of rising water levels at stationary hydrological posts in order to prevent (or minimize the risk) the transition of the territory management system under consideration to an exceptional state (emergency situation). To solve this problem, a decision support system is proposed for early prediction of water rise levels, based on a neural network (intelligent) analysis of retrospective data (code of a stationary hydrological post / automatic station, date, water level at a stationary hydrological post / automatic station, atmospheric pressure, wind speed, snow cover thickness, amount of precipitation, time and air temperature) in order to calculate the values of water levels for 5 days in advance. The artificial neural network itself is based on the freely distributed TensorFlow machine learning software library, and a modified error backpropagation method is used as training, the main difference of which is the addition of an artificial neural network (ANN) learning rate increase factor. A mathematical model of the subject area is constructed in the form of a multidimensional space and an integrated multidimensional data model. A single integrated model describes the subject area quite fully and can be used as a single source of information, which provides system information support, defined as a set of distributed databases. All this makes it possible to improve the accuracy of forecast values of water levels at stationary hydrological posts and automatic stations. The main difficulty in early forecasting is a sharp rise in water levels over a short period of time (usually 1 day) for various reasons. Our neural network model takes this into account and allows us to give more accurate results. The results of the analysis of the effectiveness of the method showed that the proposed decision support system is more accurate: the average error does not exceed 55.68 cm for the entire flood period, the average error between the real and calculated values in the framework of forecasting for 5 days does not exceed 2.10 % compared to existing common methods/systems (8.36 %). This will give the necessary time to special services to carry out anti–flood measures to prepare for the protection of technical facilities of enterprises.

Optimizing inspection plan for corroded pipeline with considering imperfect maintenance

Metal-loss corrosion may result in pipeline’s failure and expensive downtime loss. Thus, to keep the pipeline’s normal operation, it is crucial to draw up scientific inspection and maintenance plans. This study is to optimize the inspection plan for corroded pipeline. Limit state functions are established for the corrosion leakage and burst respectively, and the pipeline’s failure probability is obtained with Monte Carlo simulation. The pipeline’s failure probability is further evaluated by the copula function and with considering the correlation of different failure modes. Moreover, a hybrid failure rate model is developed to update the pipeline’s failure probability, where the age reduction factor and failure rate increase factor are adopted, and imperfect maintenance is taken into account. On this basis, an optimization model is established with the objective to minimize the total maintenance cost, and genetic algorithm is applied to optimize the inspection plans. A case study is conducted, and the optimal results are analyzed according to acceptable maximum failure probability characteristics for the areas with different risk levels. The results show that periodic inspection and perfect maintenance are suitable for high-risk areas, and the proposed inspection plan is more suitable for corroded pipeline in low or medium-risk areas.

Development of Scan Survey Adjust Method for Radioactively Contaminated Sediment in Underwater Conditions

Kim, J.-H.; Cho, G.-S.; Lee, J.-J.; Hong, S.-B.; Lee, E.-J.; Lee, B.-C., and Seo, B.-K., 2021. Development of scan survey adjust method for radioactively contaminated sediment in underwater conditions. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 325–329. Coconut Creek (Florida), ISSN 0749-0208. The nuclear power plants located near the coast and river to supply the cooling water. Therefore, unexpected radioactive materials leakage can contaminate the sediment on the coast, river, and lake. The conventional hotspot assessment methods are sampling and scan survey. The sampling method has limitations due to the sample representation, while the scan survey method is an easy way to assess hotspot distribution. However, the scan survey method can overestimate or underestimate the hotspot size depending on the radioactivity concentration, because this method determines the hotspot is exist when the measured count rate or dose rate exceeds the criteria level. In this study, the scan survey adjust method of sediment scan survey has developed. To this, the distribution of spatial effective dose rate was assessed by MCNP(Monte Carlo N-Particle) simulation. Also, the overestimation factor, which is the ratio of the actual hotspot size and estimated hotspot size was calculated. For the scan survey adjust method devised using the overestimation factor, maximum dose rate, and dose rate increase factor. Using this method, the scan survey experiment results were adjusted. As a result, 137Cs survey result that was underestimated were adjusted 11.51∼73.05% overestimate. 60Co survey result that was 2∼3 times overestimated was adjusted 2.02∼45.73% overestimate. Therefore, the scan survey adjusts method can prevent hotspot underestimation and reduced the relative error. This method can improve the accuracy of the hotspot scan survey in sediment.

Cleaning control of stencil printing subject to performance deterioration

PurposeMost solder paste printers are configured to periodically clean the stencil to maintain printing quality. However, a periodical cleaning control may result in excessive cleaning operations. The purpose of this paper is to develop a control method to schedule stencil cleaning operations appropriately.Design/methodology/approachA hybrid failure rate model of the stencil printing process with age reduction factor and failure rate increase factor is presented. A stencil cleaning policy based on system reliability is introduced. An optimization model used to derive the optimal stencil cleaning schedule is provided.FindingsAn aperiodic stencil cleaning control with good adaptability is achieved. A comparative analysis indicates that aperiodic control has better printing system reliability than traditional periodical control under the same cleaning resource consumption.Originality/valuePeriodical cleaning control commonly used in industrial printing process often results in excessive cleaning operations. By incorporating the printing system reliability, this paper develops an aperiodic stencil cleaning control method based on hybrid failure rate model of the stencil printing process. It helps to reduce unnecessary cleaning operations while keeping printing quality stable.

Multi-objective imperfect preventive maintenance optimisation with NSGA-II

Maintenance optimisation is a multi-objective problem in nature, and it usually needs to achieve a trade-off among the conflicting objectives. In this study, a multi-objective maintenance optimisation (MOMO) model is proposed for electromechanical products, where both the soft failure and hard failure are considered, and minimal repair is performed accordingly. Imperfect preventive maintenance (IPM) is carried out during the preplanned periods, and modelled with a hybrid failure rate model and quasi-renewal coefficient. The initial IPM period and the total number of IPM periods are set as the decision variables, and a MOMO model is developed to optimise the availability and cost rate concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is applied to solve the model. A case study of wind turbine’s gearbox is provided. The results show that there are 30 optimal solutions in the MOMO’s Pareto frontier that can maximise the availability and minimise the cost rate simultaneously. Compared with the single-objective maintenance optimisation, it can provide more choices for maintenance decision, and better satisfy the resource constraints and the customer’s preference. The results of the sensitivity analysis show that the effect of age reduction factor on optimisation results is greater than that of failure rate increase factor.

Acute thermal effects in an inland freshwater crab Austrothelphusa transversa (von Martens, 1868) occupying seasonal, tropical rivers

Crustaceans occupying drying waterholes in seasonal tropical rivers can access terrestrial areas to regulate hyperthermia. An experiment tank filled with water and containing a mesh ramp and dry platform was designed to determine the terrestrial locomotion response of the tropical inland freshwater crab Australothelphusa transversa (von Martens, 1868) when exposed to increasing water temperatures. A thermoline controller increased water temperature (acclimation 25 °C, ± 0.5) at two rates, 18 °C h–1 (commonly employed in other studies) and 3 °C h–1 (reflecting measured regional river conditions) to determine the acute effects temperature (AET50) threshold for the crab. Random movement was minimal in the 18 °C h–1 experiment, with crabs accessing and remaining on the platform until the experiment ended (AET50 = 40.2; maximum = 41.69 °C). Crabs exhibited more random movement in the 3 °C h–1 experiment, visiting the platform for 3–21 min before returning for 3–131 min (AET50 = 34.2, maximum = 37.25 °C). Crabs collected from a second, more arid, geographical location in northern Australia had a similar AET using 3 °C h–1 increase (AET50 = 35.3; maximum 37.60 °C) and a similar AET using 18 °C h–1 increase (AET50 = 39.3; maximum 41.7 °C) to the coastal crab results (ANCOVA interaction location and ramp rate F = 1.88, P > 0.15). A temperature rate altering AET estimates by several degrees (regardless of where crab population was collected: ANCOVA location, F = 0.668, P > 0.4; rate increase factor, F = 4.31, P < 0.001) highlights the need for laboratory experiments to be underpinned by relevant field data. These data outline that crab avoidance responses could include accessing river banks, as a response necessary to regulate temperature during critical periods.

Integration of maintenance systems

Excellence in maintenance is imperative in highly competitive market because it resulted into minimum maintenance cost, high equipment effectiveness, maximum reliability of the system, high quality of the products, low delivery time, high flexibility, safety etc. Any maintenance system such as Total Productive Maintenance (TPM) or Reliability Centered Maintenance (RCM) or Condition Based Maintenance (CBM) alone cannot achieve the excellence in maintenance but its integration may do. In this paper, an integration of TPM, RCM and CBM is proposed with a maintenance policy to take advantage of their respective strengths. A continuously monitored system subject to degradation due to the imperfect maintenance, where a hybrid hazard rate based on the concept of age reduction factor and hazard rate increase factor to predict the evolution of the system reliability in different maintenance cycles has been assumed.A quantitative decision making model for an integrated maintenance system is derived in order to assess the performance of the proposed maintenance policy. Numerical examples of calculation of optimal preventive maintenance age x and preventive maintenance number N* for the given cost ratio of corrective replacement and predictive preventive maintenance are given.

A production scheduling problem considering random failure and imperfect preventive maintenance

Many manufacturing companies are suffering from increasing machine wear with usage and age as degradation process. For a company to be successful, machines need to work continuously and make more profit in a certain period. However, machines can be unavailable due to the scheduled maintenance and unexpected failures. Hence, this study connects production scheduling with maintenance planning for a repairable system. Three types of maintenance are performed to restore the machines: (1) minimal repair is undertaken upon failure and the machine restored “as bad as old”; (2) replacement as a kind of perfect maintenance is implemented based on a cost–time rate function, which is defined to evaluate the economy of preventive maintenance action; and (3) preventive maintenance just makes the machine less deteriorated but not “as good as new.” Age reduction factor and failure rate increase factor are introduced to illustrate the imperfect character. The objective of this study is to maximize the total profit that is composed of production value, production cost, maintenance cost (including the preventive maintenance cost, replacement cost, and minimal repair cost for random failure), and lateness cost (which is contributed by the job sequence and maintenance activities). Finally, the proposed model is validated by a numerical example.

Sequential imperfect preventive maintenance policy with random maintenance quality under reliability limit

To determine the optimal maintenance number for a system with random maintenance quality in infinite time horizon, a sequential imperfect preventive maintenance model considering reliability limit is proposed. The proposed model is derived from the combination of the Kijima type virtual age model and the failure rate adjustment model. Maintenance intervals of the proposed model are obtained through an iteration method when both failure rate increase factor and maintenance restoration factor are random variables with a uniform distribution. The optimal maintenance policy is presented by minimizing the long-run average cost rate. A real numerical example for the failures of numerical control equipment is given to demonstrate the proposed model. Finally, a discussion is presented to show how the optimal average cost rate depends on the different cost parameters. The results show that in order to satisfy the practical requirements of high reliability, it is necessary and worthwhile to consider the system's reliability limit in preventive maintenance practice.

Analysis of Dynamic Response of Concrete Using a Mesoscale Model Incorporating 3D Effects

Shock and impact loading often generates stress waves in the responding structural object with a drastic time and spatial variation. Consequently the transient response of the material involves not only high strain rate, but also a highly non-homogeneous stress field with respect to the mesoscale heterogeneity of the concrete-like materials. To comprehensively capture the material response and the underlying mechanisms under such loading conditions, the use of a mesoscale model is desirable. This paper discusses the development of a mesoscale model that describes reasonably the random mesoscopic structure of concrete materials and at the same time allows for the simulation of response under complex dynamic loads. Due to the complexity in the generation and meshing of the random aggregate structure of concrete, at the present stage the random mesoscale geometry is generated within a 2D plane. The use of a 2D representation of a concrete is common and may be acceptable in lower loading rate applications, however, under high rate loading a 2D model for concrete in compression could introduce noticeable errors due to an inability to fully capture the crucial lateral inertia confining effect. This special phenomenon is investigated in this paper, and a remedial modelling scheme is proposed to minimize the inaccuracy in a 2D mesoscale model for high strain rate loading. Finally, the experimentally observed strain rate increase factor is discussed in light of the mesoscale numerical modeling results.

Reliability-centered predictive maintenance scheduling for a continuously monitored system subject to degradation

This paper tries to integrate sequential imperfect maintenance policy into condition-based predictive maintenance (CBPM). A reliability-centered predictive maintenance policy is proposed for a continuously monitored system subject to degradation due to the imperfect maintenance. It is assumed that the system hazard rate is a known function of the system condition and then can be derived directly through CBPM. A hybrid hazard rate recursion rule based on the concept of age reduction factor and hazard rate increase factor is built up to predict the evolution of the system reliability in different maintenance cycles. Whenever the system reliability reaches the threshold R, an imperfect preventive maintenance (PM) is performed on the system. The optimal reliability threshold R is determined by minimizing the cumulative maintenance cost per unit time in the residual life of the system which is based on simulation. Finally, a discussion is presented to show how the optimal results depend on the different cost parameters.

Evaluation Procedures for Irradiation Effects and Sodium Environmental Effects for the Structural Design of Japanese Fast Breeder Reactors

In the structural design of fast breeder reactors, irradiation effects and sodium environmental effects on structural materials have to be taken into account. In this paper, firstly, an evaluation procedure for irradiation effects on the mechanical properties of 316FR (FBR Grade 316 stainless steel), which is a newly developed stainless steel for the Japanese demonstration fast breeder reactor, is proposed. The procedure gives a limit of accumulated fast neutron fluence E&gt;0.1 MeV as a function of temperature, so that the minimum tensile fracture elongation of 10 percent, which is the threshold for material to stay ductile, is maintained. Furthermore, the procedure determined a creep life reduction factor and a creep rate increase factor as a function of accumulated thermal neutron fluence E&lt;0.4 eV, within the limitation of the accumulated fast neutron fluence, to account for the creep life reduction and the increase of creep rate due to irradiation. Secondly, an evaluation procedure for sodium environmental effects on the integrity of 316FR and modified 9Cr-1Mo steel was proposed. It gave a corrosion allowance as a function of temperature, oxygen content, and service time, based on corrosion tests. It determined that no correction factors that correspond to sodium environment on design allowable stresses, etc., are needed, because no adverse effects of sodium on the mechanical properties of 316FR and modified 9Cr-1Mo steel were to be expected in the service conditions of FBRs. Both the procedures have been incorporated into the Japanese Elevated Temperature Structural Design Guide for Demonstration Fast Breeder Reactor.

Parameter tuning of rate-based congestion control algorithm for ABR service class in ATM networks

A rate-based congestion control algorithm has been developed and standardized in the ATM forum for ABR service class. In the standard the behaviour of source and destination end systems is specified by several control parameters such as RIF (rate increase factor) and RDF (rate decrease factor). In spite of the fact that the performance of the rate-based congestion control algorithm depends heavily on the selection of these control parameters, no selection method of parameters is shown in the standard. In this paper, by extending our previous work, appropriate settings of rate-control parameters in the various circumstances are investigated. We first analyse the dynamical behaviour of the rate-based congestion control for multiple groups of ABR connections with different propagation delays. Next we evaluate the effect of CBR traffic on ABR connections. Simulation results for a multihop network configuration are also presented to exhibit the trade-off relationship among cell loss probability, link utilization and fairness. Finally, a selection method of control parameters in the multihop network is proposed based on our analytic methods and simulation results © 1998 John Wiley & Sons, Ltd.

Parameter tuning of rate‐based congestion control algorithm for ABR service class in ATM networks

A rate-based congestion control algorithm has been developed and standardized in the ATM forum for ABR service class. In the standard the behaviour of source and destination end systems is specified by several control parameters such as RIF (rate increase factor) and RDF (rate decrease factor). In spite of the fact that the performance of the rate-based congestion control algorithm depends heavily on the selection of these control parameters, no selection method of parameters is shown in the standard. In this paper, by extending our previous work, appropriate settings of rate-control parameters in the various circumstances are investigated. We first analyse the dynamical behaviour of the rate-based congestion control for multiple groups of ABR connections with different propagation delays. Next we evaluate the effect of CBR traffic on ABR connections. Simulation results for a multihop network configuration are also presented to exhibit the trade-off relationship among cell loss probability, link utilization and fairness. Finally, a selection method of control parameters in the multihop network is proposed based on our analytic methods and simulation results © 1998 John Wiley & Sons, Ltd.