One of major errors in flow rate measurement for two-phase flow using an Electrical Capacitance Sensor (ECS) concerns sensor sensitivity under temperature raise. The thermal effect on electrical capacitance sensor (ECS) system for air-water two-phase flow monitoring include sensor sensitivity, capacitance measurements, capacitance change and node potential distribution is reported in this paper. The rules of 12-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance and sensitivity map the basis of Air-water two-phase flow permittivity distribution and temperature raise are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. The cross-sectional void fraction as a function of temperature is determined from the scripting capabilities in ANSYS simulation. The results show that the temperature raise had a detrimental effect on the electrodes sensitivity and sensitive domain of electrodes. The FE results are in excellent agreement with an experimental result available in the literature, thus validating the accuracy and reliability of the proposed flow rate measurement system.
The four tank system is a widely used mechatronic laboratory system in control theory. This work is aimed to choose the best controller for the four tank system (4TS) with two input force. The optimal control is one of the best techniques in a sense of performance, and is demonstrated for the level control of 4TS. There are several controller systems in optimal control for this purpose which are Linear Quadratic Regulator (LQR), Linear Quadratic Gaussian Regulator (LQGR), H2, and H∞ controller system. These controllers will be applied to this important mechatronic system (4TS) separately, and compared the performance for disturbance rejection with each other to study the effect of these controller systems on the 4TS controlled state. On the other hand the performances of the optimal control systems are compared with other controller performances available in literatures for the same case study. The results indicate that the Linear Quadratic Regulator (LQR) provides significant improvement over completely controllers. The simulations were carried out in MATLAB-Simulink.
The aiming of this work is to enhancement the structural health monitoring (SHM) framework of beams structure for damage detection to treatment the drawbacks of poor detection efficiency in traditional of beams monitoring algorithms, the improvement framework on beams SHM is based on novel data classification technique through designing the k-Nearest Neighbor (k-NN) algorithm. First, the beam finite element model under impact load is analysis, and the cumulative damages are considered and introduced to beam model. The datasets of beam SHM are compiled from the sensors installed in beam structure, and then processed using kernel principal component analysis to remove the unnecessary features and reduce the scale of classification features. The k-NN algorithm parameters of beam SHM are determined by the genetic optimization algorithm (GOA) to establish the optimal SHM classification model of beam. Finally, a comparison between the present damage detection results via k-NN and traditional models via convolutional neural network (CNN) and supper vector machine (SVM) results available in literatures is established through the most significant indexes of testing to check the effectiveness and superiority of suggested method. The results show that the presented SHM model are gave higher precision, reduced the time of modeling, and improvement the total performance of damage detection model in beams. The current performance are recorded 95.3%, 91.8%, and 89.7%, for accuracy rate, recall rate, and F-score respectively.
As mentioned, transportation systems have become a fundamental base for the economic growth of all nations. Nevertheless, many countries around the world are
Open access peer-reviewed chapter
Modern machine tools with high speed machining capabilities could place rotating shafts, gears, and bearings under extreme thermal, static, and impact stresses, potentially increasing their failure rates. In this research, a gearbox damage detection strategy based on discrete wavelet transform (DWT), wavelet packet transform (WPT), support vector machine (SVM), and artificial neural networks (ANN) is presented. Three case studies are conducted to compare the classification performance of SVM kernel functions and ANN. First, a fault detection analysis based on DWT and WPT is carried out to extract the damage information from the gearbox’s raw vibration signal. In this step, wavelet coefficients obtained from DWT are characterized using statistical calculations. Energy characteristics of the gearbox signal are acquired using WPT and their statistical characteristics are also computed. These three sets of information extracted from wavelet transforms are utilized as the input to SVM and ANN classifiers. Secondly, the improved distance evaluation technique (IDE) is implemented to select the sensitive input features for SVM and ANN. The penalty parameter C and kernel parameter γ in SVM are also optimized using the grid-search method. Finally, the optimized features and parameters are input into SVM and ANN algorithms to detect gearbox damage. The result shows that gearbox damage detection using energy characteristics extracted from WPT (Case 2) or their statistical values as input features (Case 3) to the learning algorithms produces higher classification accuracies than using statistical values of the DWT coefficients as inputs (Case 1). In addition, RBF-SVM has the best classification performance in Case 2 and 3 while Linear-SVM has the best classification accuracy rate in Case 1 in damage detection average.
Undoubtedly, infrastructure is the backbone of the world's economies. This includes transportation networks, bridges, tunnels, subways, railways, shipyard cranes, water delivery systems, utilities, dams, various pipeline networks, power transmission, communication network, government centers, and large business centers, to cite a few examples. Resilience is fundamentally a theoretical concept. Yet ongoing and warranted reflection regarding this concept in the context of disaster and emergency management and mitigation, crisis management, and the protection of critical infrastructure, for instance, has brought this it into the policy making arena, where considerations concerning its practical applications are becoming important. While difficult, given the complexity of resilience, and its definitional ambiguity, the ability to assess such a concept helps to bridge the gap between theory and application, and between academic and policy circles. This chapter introduces an overview of the intelligent and resilient urban infrastructures to support smart cities. It discusses and compares different definitions of the resilience infrastructure and emerging and relevant topics such as big data (BD) and data mining (DM), methodologies for their implementation and the opportunities, challenges, and benefits of incorporating resilience using BD applications for smart cities. In addition, it endeavors to identify the requirements that support the implementation of resilience applications for smart city services. The review reveals that several opportunities are available to apply to the resilience of infrastructure in smart cities. However, there are still many issues and challenges to be addressed to achieve a better utilization of this concept, and the application of new methodologies for the evaluation of resilient systems that can provide specific and satisfactory results for resilience evaluation.
A critical problem facing data collection in structural health monitoring, for instance via sensor networks, is how to extract the main components and useful features for damage detection. A structural dynamic measurement is more often a complex time-varying process and therefore, is prone to dynamic changes in time-frequency contents. To extract the signal components and capture the useful features associated with damage from such non-stationary signals, a technique that combines the time and frequency analysis and shows the signal evolution in both time and frequency is required. Wavelet analyses have proven to be a viable and effective tool in this regard. Wavelet transform (WT) can analyze different signal components and then comparing the characteristics of each signal with a resolution matched to its scale. However, the challenge is the selection of a proper wavelet since various wavelets with varied properties that are to analyze the same data may result in different results. This article presents a study on how to carry out a comparative analysis based on analytic wavelet scalograms, using structural dynamic acceleration responses, to evaluate the effectiveness of various wavelets for damage detection in civil structures. The scalogram’s informative time-frequency regions are examined to analyze the variation of wavelet coefficients and show how the frequency content of a signal changes over time to detect transient events due to damage. Subsequently, damage-induced changes are tracked with time-frequency representations. Towards this aim, energy distribution and sharing information are investigated. The undamaged and damaged simulated comparative results of a structure reveal that the damaged structure were shifted from the undamaged structure. Also, the Bump wavelet shows the best results than the others.
Sliding bearings are bearings that work under sliding friction, used in fast rotating rotor machines or planetary gears. The sliding bearing works smoothly, reliably, and without noise. Under the condition of liquid lubrication, the sliding surface is separated by lubricating oil without direct contact, which can also greatly reduce friction loss and surface wear, and the oil film also has a certain ability to absorb vibration. The part of the shaft supported by the bearing is called the journal, and the part that matches the journal is called the bearing bush. The theoretical models of the bearing should precisely describe real operating conditions of the bearing. In initial calculations, simplified models are often used. This chapter describes the design process for sliding bearings, depicting the particular design stages in the form of a structural chart with calculation examples, as well as design examples and solved/unsolved problems. This chapter can be used as teaching material for courses in machine design for mechanical and industrial engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
The acoustic muffler development depends on optimizing its volume for high performance, it is of great importance in the industrial field to obtain a reduction in duct noise economically and efficiently. The main aim of this work is to optimize analytically the...
The shaft is one of the important parts that make up the machine. Its main function is to support the transmission parts (such as gears, worm gears, etc.) for rotary motion and to transmit motion and power. According to the different loading conditions of the shaft, it can be divided into three types: the rotating shaft, the transmission shaft, and the mandrel. The structural design of the shaft includes determining a reasonable shape and all structural dimensions of the shaft. When designing the shaft, it is necessary to make its structure easy to process, measure, disassemble, and maintain, and strive to reduce the amount of labor required and improve labor productivity. The diameter required for each shaft section is related to the magnitude of the load on the shaft. This chapter examines shaft dimension design examples, and discusses the shaft design process from the aspects of force analysis and stress analysis, to increase the reader's understanding of correct shaft dimension design. Examples and solved/unsolved problems for computational shaft design are also presented. This chapter can be used as teaching material for courses in machine design for mechanical, industrial, and materials engineering majors in colleges and universities, and can also be used as a reference for scientific and technical personnel engaged in scientific and engineering calculations.
The objective of this study is to predict the fatigue life of carbon fibre/epoxy composite laminate sheets involving 12 balanced woven bidirectional layers with the same orientation angle [0/90°]. The composite sheets considered are subjected to variable amplitude block loadings with different negative and positive stress ratios. This objective is accomplished by designing an efficient artificial neural network (ANN) architecture, with taking into account effect of the residual strength from spectrum loading. The number of cycles to failure (N) is related to the residual strength of the structure for constant amplitude loading. A simple first order model is postulated that determines the residual strength at any point during the fatigue life as a function of the static strength and stress ratio by applying the two-parameter Weibull probability density distribution. Two neural network structures, a feed-forward neural network (FFNN) and a radial basis neural network (RBNN), are applied, trained and tested to predict the fatigue life based on four groups of data considered. These data include the maximum stress (σmax) and the stress ratio (R), the Smith-Watson-Topper (SWT) parameter, fatigue strength ratio (Ψ), or failure criterion with the fibre orientation. On the other hand, the validity of the SWT, Ψ and selected suitable failure criterion for present study including material, loading and orientation were checked and modified before they were used for training the designed ANNs. The results show improvement when using one input data (SWT, or Ψ, or failure criterion) instead of two input data (σmax and R) and for the case of one input data the best prediction is observed for failure criterion condition, followed by Ψ and SWT respectively. Moreover, the RBNN demonstrates better results as compared with those obtained by the FFNN.
The present work develops an expert system for detecting and predicting the crude oil types and properties at normal temperature <TEX>${\theta}=25^{\circ}C$</TEX>, by evaluating the dielectric properties of the fluid transfused inside glass fiber reinforced epoxy (GFRE) composite pipelines, by using electrical capacitance sensor (ECS) technique, then used the data measurements from ECS to predict the types of the other crude oil transfused inside the pipeline, by designing an efficient artificial neural network (ANN) architecture. The variation in the dielectric signatures are employed to design an electrical capacitance sensor (ECS) with high sensitivity to detect such problem. ECS consists of 12 electrodes mounted on the outer surface of the pipe. A finite element (FE) simulation model is developed to measure the capacitance values and node potential distribution of ECS electrodes by ANSYS and MATLAB, which are combined to simulate sensor characteristic. Radial Basis neural network (RBNN), structure is applied, trained and tested to predict the finite element (FE) results of crude oil types transfused inside (GFRE) pipe under room temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an RBNN results, thus validating the accuracy and reliability of the proposed technique.
