An Improved Method for Diagnosis of Induction Motor Load Mechanical Unbalance Fault Using Current Signal Analysis

Most induction motor problems are due to load instabilities. Load problems mainly influence on system mechanical properties. Therefore, it is preferred to detect these problems using mechanical sensors (e.g. accelerometer, velocity sensor, etc.). However, all motors and loads are not accessible, and remote monitoring of systems can be helpful. Motor current signature analysis (MCSA) is a remote monitoring method that has developed recent years. The aim of the current paper is to evaluate the ability of MCSA to detect centrifugal fan faults. It tries to show how to distinguish between unbalanced load and other types of induction motor unbalances based on MCSA method. In the current study, condition monitoring methods are improved by reformulating the current equations. These results come from a large research with the objective population of 60 induction motors in the alloy steel industry and here three case studies are used to check the theoretical results.     

A Novel Approach to Optimize Power Consumption in Orchard WSN: Efficient Opportunistic Routing

Wireless Personal Communications - In orchard wireless sensor networks (WSN) application, the network link quality is unstable due to the shielding effect caused by the growth of branches and...     

Big Data Based Self-Optimization Networking in Next Generation Mobile Networks

Using self-optimization techniques is the only viable solution for increasing the efficiency in next generation mobile networks. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to microcell and femto-cell users, considering the limited resources in radio access networks. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process. Based on the meaningful extracted information, the SON decision maker will be able to adjust network parameters and resource allocation factors in a more intelligent manner. The experimental results show that despite the tremendous volume of the analyzed data—which is hundreds of times bigger than usual methods—it is possible to improve the KPIs, such as throughput, up to 30% by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25% when the number of parameters is too high. Moreover, numerical results indicate the superiority of using support vector machine learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15% increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme.     

Co-precipitated Copper Zinc Oxide Catalysts for Ambient Temperature Carbon Monoxide Oxidation: Effect of Precipitate Aging Atmosphere on Catalyst Activity

A study of the effect of the aging atmosphere on the activity of co-precipitated copper zinc oxide catalysts for the ambient temperature oxidation of carbon monoxide is described and discussed. Four aging atmospheres are reported: air, N₂, H₂ and CO₂, and both the precipitation and the aging of the precipitate were carried out by flowing these gases through the precipitation cell at constant pH and temperature. For all atmospheres, the surface area of the final CuO-ZnO catalyst increases with aging time and, consequently, the specific activity (mol CO converted/g catalyst/h) also increases. However, the intrinsic activity (mol CO converted/m²/h) initially decreases with aging time before attaining a steady level. The highest activity catalysts were obtained using air as the aging atmosphere and TPR studies indicate that this catalyst is less readily reduced. Catalysts prepared using CO₂ as the aging atmosphere have lower activity, although the surface areas of these catalysts are not markedly lower. The study demonstrates that selection of the appropriate aging atmosphere, as well as the aging time, is an important parameter for the preparation of co-precipitated catalysts.     

How do people make a decision on bottled or tap water? Preference elicitation with nonparametric bootstrap simulations

People in the United States drink almost four times the amount of bottled water than they did 20 years ago, even though tap water supplies in the United States are considered to be among the safest in the world. To understand ‘how do people make a decision on tap or bottled water’, a consumer preferences survey was administered to the Civil and Environmental Engineering students attending a US university. The survey elicited information on participants’ preferences and real life attitude/preferences and included a multi‐criteria pairwise comparison. The pairwise comparison preferences were further analysed by integrating nonparametric bootstrap simulations to determine the underlying uncertainty. The results revealed that although safety issues were deemed most important, participants were also subconsciously aware of other crucial issues related to drinking water. These findings provide useful information for drinking water policy experts and water utilities about consumer perceptions of the relative virtues of tap and bottled water.     

Effect of soil properties for prediction of energy consumption in land levelling irrigation

ABSTRACT

Energy conservation is regarded as one of the most important factors in the agricultural sector due to its relation to pollution which is a result of fossil fuel (particularly gasoline) usage. The objective of this research was to develop three methods including Artificial Neural Network (ANN), regression and Adaptive Neural-Fuzzy Inference System (ANFIS) to predict the effect of soil properties on the environmental indicators in land levelling and to analyse the sensitivity of these parameters. The acquired data were used to develop accurate models for fuel energy (FE), total machinery cost (TMC) and total machinery energy (TME). The results showed that four parameters of tillage depth, forward speed, cone index and cut/fill volume had significant effects on energy consumption. ANFIS and ANN had a satisfactory performance in predicting the aforementioned parameters in the various field conditions. The ANN had the most capability in FE prediction according to the least RMSE and the highest coefficient of determination (R²) values 0.0206 and 0.9983, respectively. The ANFIS model had the most capability in the prediction of the environmental and energy parameters with the least RMSE and the highest R2 for TMC, 0.0287 and 0.9966, and for TME, 0.0157 and 0.9990, respectively.     

Kinetic study of CO hydrogenation on the MgO supported Fe–Co–Mn sol–gel catalyst

The kinetic of the Fischer–Tropsch synthesis over the MgO supported Fe–Co–Mn catalyst prepared using sol–gel procedure, was investigated in a fixed bed micro-reactor. Experimental conditions were varied as follow: reaction pressure 5–20 bar, reaction temperature 220–250 °C, H₂/CO feed molar ratio of 0.67–2 and space velocity range of 2400–3600 h^?1. 18 models according to the Langmuir–Hinshelwood–Hougen–Watson (LHHW) type rate equation were derived, and the reaction rate is fitted fairly well by one kinetic expressions based on LHWW mechanism. The kinetic parameters were estimated with non-linear regression method. The activation energy was obtained 110.9 kJ/mol for the best-fitted model.     

Intrinsic kinetics of the Fischer-Tropsch synthesis over an impregnated cobalt-potassium catalyst

The optimal amount of 15 wt%Co/10 wt%K/Al₂O₃ catalyst was prepared using the impregnation technique in order to study the kinetics of the Fischer-Tropsch synthesis. The rate of synthesis was measured in a fixed-bed micro reactor with H₂/CO feed ratio of 1–3 and space velocity in the range of 2,700–5,200 h⁻¹ under reactor pressure of 8 bar and a temperature range of 210–240 °C. The experimental data were best fitted by a Langmuir-Hinshelwood-Hougen-Watson (LHHW) approach rate in the form of - r_CO = (k₂ K₁ P_CO P_H2 )/(1 + K₁ P_CO ) - r_{CO} = (k_2 K_1 P_{CO} P_{H_2 } )/(1 + K_1 P_{CO} ). Furthermore, the data were fitted fairly well by a power law equation in the form of - r_CO = kP_CO^1.32 P_H2 ^1.42 - r_{CO} = kP_{CO}^{1.32} P_{H_2 }^{1.42} . The activation energies for LHHW approach model and power law equation were obtained as 138.5 kJ/mol and 87.39 kJ/mol, respectively.     

Influence of the sunflower oil content,cooking temperature and cooking time on the physical and sensory properties of spreadable cheese analogues based on UF white‐brined cheese

In this study, a cheese analogue was produced based on UF white‐brined cheese by replacing the dairy fat with sunflower oil and cooking at different temperatures and times. Hardness, adhesiveness, meltability and oiling‐off were used to evaluate the suitability of the final product. Partial replacement of dairy cream with sunflower oil led to an increase in the adhesiveness, oiling‐off and spreadability while decreasing the hardness of the cheese. Optimum operating conditions were found to be 85 °C for the cooking temperature, 14.12 min for the cooking time and 27.14% w/w for the sunflower oil content.