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Go to Editorial ManagerThe problem that still exists nowadays with the petrol station is the method of operation because the petrol station is currently operated manually. As it is a time-consuming process that increases manpower, other problems are related to accuracy, gasoline smuggling, fluctuations in global oil prices, sales, database management, environmental pollution and others. Traditional methods of monitoring fuel in petrol station by humans on site are unable to meet the expectations for efficiency, accuracy and cost. Therefore, this paper designs an intelligent system of three filling stations, where the three stations are simultaneously displayed on a single web application, and this IoT-based system is implemented to address all the problems. Therefore, this paper presents the design and implementation of three petrol stations in which we are going to measure the level of fuel and show it to central server. internet of things (IoT) based petrol station monitoring system is a good approach to improve monitoring efficiency and to improve management efficiency in stations remotely. simulation results presented in LabVIEW software showed the ability of the system to monitor levels of petrol, detect fire, evaporation and etc.
Monitoring the health of rotating machinery is essential to ensure system safety, achieve cost savings, and enhance overall reliability. The requirement for a reliable and clear method of identifying defects has prompted the development of several monitoring techniques. They utilize vibration, measurement of the motor's current signature, and acoustic emission data in the process of condition monitoring. The MFS (machinery fault simulator) equipment was used to determine bearing faults using vibration signal analysis. MFS conducts simulations and investigations of many bearing issues, including those occurring in the inner race, outer race, and balls. An accelerometer (type B & K 4366) was connected to a data acquisition device (IDAC-6C) to record vibration signals under different operating conditions. Furthermore, a tachometer equipped with an LCD display is employed to measure the rotational speed. Four types of defects in ball bearing (Koyo 1205C3 type) were studied, the slot in outer race with size 0.196 mm, the slot in inner race with size 0.191 mm, in ball with size 0.196 mm in additional to compound defect. In this paper, spectral correlation technique was employed to detect defects in ball bearings running at varying speed, along with spectral coherence and the corresponding Enhanced Envelope Spectrum (EES) in frequency-order domain and order-order domain. The results show that the adopted methods, that are used to analyze the real vibration signals for diagnosis the defected ball bearings, are suitable, accurate and less processing time for varying speed. The processing time of the FastACP method used to analyze the signals in order- order domain is less than that of the adopted method in the frequency-order domain for any defect type. Overall, using the FastACP method in the order-order domain significantly reduced processing time by approximately 27% compared to the adopted method in the frequency- order domain under varying speed conditions.
This paper presents a diagnostic technique for power quality analysis against different disturbances in electrical power source. The presented technique utilizes a wavelet packet transform (WPT)-based a proposed algorithm for monitoring and detection various disturbances occurring in supply voltage signal and in supply frequency. The values and the time locations for low and high frequency coefficients are determined up to level six and compared with a threshold determined from the operation of healthy source. The proposed technique is tested on certain cases and the simulated results indicate that this technique is effective for detecting and monitoring different mentioned disturbances.
Gas flow measurements are pivotal in several medical applications. For instance, mechanical ventilators and respiratory monitoring applications need flowmeters with strict requirements. This study is concerned with a three-dimensional computational fluid dynamics (CFD) analysis. The CFD methodology was confirmed by analyzing the flow characteristics of flexible membrane with trapezoidal orifice plates. Variable area orifice meters (VAOMs) are increasingly being embraced in respiratory monitoring applications, employed in the context of mechanical ventilation within medical settings. Each system integrates a flexible orifice plate within the conduit. The simulations are conducted considering realistic deformations in structure through two-way fluid-structure interactions (FSI) using the Arbitrary-Lagrangian-Eulerian (ALE) approach. This research paper analyzes using the finite volume method (FVM). A thorough numerical simulation was performed for the turbulence models. The orifice's thickness and shape significantly influence pressure drop and deflection.
This paper presents an extensive review of energy harvesting from the column vibrations of wind turbines under the influence of wind. The study investigates the underlying theories, mechanisms, and potential applications of such a system. By tapping into the vibrational energy otherwise dissipated in wind turbines, the study proposes an innovative approach to enhance renewable energy generation. Furthermore, the potential benefits of the technology, such as powering remote sensors, vibration damping, structural health monitoring, and increasing wind turbine efficiency and lifespan, are discussed. While the study acknowledges the promise of such an approach, it also emphasizes the need for further research to optimize and integrate these systems effectively into the renewable energy landscape.
Recently, Internet of Thing technology has been used to develop numerous applications, this paper compromising design and implementation of greenhouse prototype that integrated with the IoT to adjust the system’s parameters and monitor the system status from any place in this world. This system involves three intelligent controllers that designed to stabilize the temperature degree, water level in soil, and light intensity inside the greenhouse prototype structure. These systems have been built by two important parts: the hardware and software. The hardware part could be achieved by designing and implementing the control circuits, actuators, and install the sensors as well as the devices. The second one is the software part which is involves implementing Fuzzy Inference Engine that represent the system’s brain that monitor and manage the entire process in the system to ensure the best performance. This system has been built to contain three control systems that means there are three different Fuzzy controllers. In order to keep the system practicality, the fuzzy controllers should be aggregated in single code that resides in single microcontroller chip with additional codes that perform the IoT duties. The proposed IoT system provides the ability for specific people to monitor and manage their systems remotely, using a web application with cloud technology. The major contributions of the proposed system are started by downloading the controller’s set-points (the desired environmental conditions) from the web page, transfer the set- points to the controllers, and upload data that read from sensors to the same web page.)
Safwan-Zubair area is regarded as one of the important agricultural areas in Basrah province, South of Iraq. The aim of this study is to predict groundwater level in this area using ANNs model. The data required for building the ANNs model are generated using MODFLOW model (V.5.3). MODFLOW model was calibrated based on field measurements of groundwater level in 13 monitoring wells during a period of one year (Nov./2013 to Oct/2014). The neural network toolbox available in MATLAB version 7.1 (2010B) was used to develop the ANN models. Three layers feed-forward network with Log- sigmoid transfer function was used. The networks were trained using Levenberg-Marquradt back-propagation algorithm. The ANN modes are divided into two groups, each of four models. The input data of the first group include hydraulic heads, while, the input data of the second group include hydraulic heads and recharge rates. Based on results of this study it was found that; the best ANN model for predicting groundwater levels in the study area is obtained when the input data includes hydraulic heads and recharge rates of two successive months preceding the target month, the best structure of ANN model is of three layers feed-forward network type composes of two hidden layers, each of ten nodes, and the including of recharge rates as input data, beside the hydraulic heads has improved slightly the results.
In this paper, a Neural Network (NN) model system for self-organization fish school system is identified. Monitoring and data extraction from fish school video has been achieved by using image processing technique in order to generate the data suitable for parameter identification of NN model system. Data obtained have been used to identify the parameters of a model based on a black-box represented by nonlinear autoregressive exogenous model (NARX). The obtained results show that this system can be used for multi robot formation system.
The aim of the present paper is to investigate buckling phenomenon of various cracked plates under compression load. The finite element procedure (ANSYS Package) is used to determine the critical buckling load by considering the effects of crack length and crack location (i.e. crack parameters) as well as loading direction parallel or perpendicular with respect to crack faces. It is found from the obtained results which are summarized graphically in figures that the crack parameters and loading direction have significant effects on the critical buckling load (i.e. increased or decreased) of compressed cracked plates. The effects of these factors are discussed in detail. The useful and interesting conclusions drawn from this work will be helpful for health monitoring or condition assessment of aging plated structures with cracking damages.
Groundwater in arid and semi-arid regions, such as the studied area (Safwan Al-Zubair area, south of Iraq), is of specific meaning as a major source for domestic use and irrigation demand. There is a need to better understand the interactions between groundwater and surface water (Shatt Al-Basrah Canal). These interactions can negatively affect the quality of groundwater in this area, especially that the water of Shatt Al- Basrah Canal contains highly concentrated pollutants. The aim of the study is to investigate the temporal disparity of river-aquifer interactions and count the amount of river interchange among canal and aquifer. In this research, a new concept of paradigm will be advanced utilizing RIVER package of Groundwater River Paradigm (MODFLOW) for the simulation of river-aquifer interaction operations. Six monitoring wells are chosen to evaluate the preliminary and historical groundwater hydraulic heads for six months and then use all collected data in Modflow to execute the simulation of numerical modeling to assessment the interaction between surface water and groundwater. The amount of seepage out from the canal towards the aquifer was (64.99 m 3 /day) in wet season (winter season), as a result of the high levels of the surface water compared to the hydraulic heads of groundwater. The amount of seepage in dry season towards the aquifer is equal to (336.8 m 3 /day).