To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Voice Identity Finder Using the Back Propagation Algorithm of an Artificial Neural Network

AbstractVoice recognition systems are used to distinguish different sorts of voices. However, recognizing a voice is not always successful due to the presence of different parameters. Hence, there is a need to create a set of estimation criteria and a learning process using Artificial Neural Network (ANN). The learning process performed using ANN allows the system to mimic how the brain learns to understand and differentiate among voices. The key to undergo this learning is to specify the free parameters that will be adapted through this process of simulation. Accordingly, this system will store the knowledge processed after performing the back propagation learning and will be able to identify the corresponding voices. The proposed learning allows the user to enter a number of different voices to the system through a microphone

Aircraft Cabin Noise Minimization Via Neural Network Inverse Model

This paper describes research to investigate an artificial neural network (ANN) approach to minimize aircraft cabin noise in flight. The ANN approach is shown to be able to accurately model the non-linear relationships between engine unbalance, airframe vibration, and cabin noise to overcome limitations associated with traditional linear influence coefficient methods. ANN system inverse models are developed using engine test-stand vibration data and on-airplane vibration and noise data supplemented with influence coefficient empirical data. The inverse models are able to determine balance solutions that satisfy cabin noise specifications. The accuracy of the ANN model with respect to the real system is determined by the quantity and quality of test stand and operational aircraft data. This data-driven approach is particularly appealing for implementation on future systems that include continuous monitoring processes able to capture data while in operation

Evaluation of Different Fault Diagnosis Methods and Their Applications in Vehicle Systems

A high level of automation in vehicles is accompanied by a variety of sensors and actuators, whose malfunctions must be dealt with caution because they might cause serious driving safety hazards. Therefore, a robust and highly accurate fault detection and diagnosis system to monitor the operational states of vehicle systems is an indispensable prerequisite. In the area of fault diagnosis, numerous techniques have been studied, and each one has pros and cons. Selecting the best approach based on the requirements or usage scenarios will save much needless work. In this article, the authors examine some of the most common fault diagnosis methods for their applicability to automated vehicle systems: the traditional binary logic method, the fuzzy logic method, the fuzzy neural method, and two neural network methods (the feedforward neural network and the convolutional neural network). For each approach, the diagnosis algorithms for vehicle systems were modeled differently. The analysis of the detection capabilities and the suitable application scenarios of each fault diagnosis approach for vehicle systems, as well as recommendations for selecting different methods for various diagnosis needs, are also provided. In the future, this can serve as an effective guide for the selection of a suitable fault diagnosis approach based on the application scenarios for vehicle systems

Computational and Numerical Simulations

Computational and Numerical Simulations is an edited book including 20 chapters. Book handles the recent research devoted to numerical simulations of physical and engineering systems. It presents both new theories and their applications, showing bridge between theoretical investigations and possibility to apply them by engineers of different branches of science. Numerical simulations play a key role in both theoretical and application oriented research

Rehabilitation Engineering

Population ageing has major consequences and implications in all areas of our daily life as well as other important aspects, such as economic growth, savings, investment and consumption, labour markets, pensions, property and care from one generation to another. Additionally, health and related care, family composition and life-style, housing and migration are also affected. Given the rapid increase in the aging of the population and the further increase that is expected in the coming years, an important problem that has to be faced is the corresponding increase in chronic illness, disabilities, and loss of functional independence endemic to the elderly (WHO 2008). For this reason, novel methods of rehabilitation and care management are urgently needed. This book covers many rehabilitation support systems and robots developed for upper limbs, lower limbs as well as visually impaired condition. Other than upper limbs, the lower limb research works are also discussed like motorized foot rest for electric powered wheelchair and standing assistance device

Optimal control for a prototype of an active magnetic bearing system

First applications of the electromagnetic suspension principle have been in experimental physics, and suggestions to use this principle for suspending transportation vehicles for high-speed trains go back to 1937. There are various ways of designing magnetic suspensions for a contact free support, the magnetic bearing is just one of them [BCK+09]. Most bearings are used in applications involving rotation. Nowadays, the use of contact bearings solves problems in the consumer products, industrial machinery, or transportation equipment (cars, trucks, bicycles, etc). Bearings allow the transmition of power from a motor to moving parts of a rotating machine [M+92]. For a variety of rotating machines, it would be advantageous to replace the mechanical bearings for magnetic bearings, which rely on magnetic elds to perform the same functions of levitation, centering, and thrust control of the rotating parts as those performed by a mechanical bearing. An advantage of the magnetic bearings (controlled or not) against purely mechanical is that magnetic bearings are contactless [BHP12]. As a consequence these properties allow novel constructions, high speeds with the possibility of active vibration control, operation with no mechanical wear, less maintenance and therefore lower costs. On the other hand, the complexity of the active (controlled) and passive (not controlled) magnetic bearings requires more knowledge from mechanics, electronics and control [LJKA06]. The passive magnetic bearing (PMB) presents low power loss because of the absence of current, lack of active control ability and low damping sti ness [FM01, SH08]. On the other hand, active magnetic bearing (AMB) has better control ability and high sti ness, whereas it su ers from high power loss due to the biased current [JJYX09]. Scientists of the 1930s began investigating active systems using electromagnets for high-speed ultracentrifuges. However, not controlled magnetic bearings are physically unstable and controlled systems only provide proper sti ness and damping through sophisticated controllers and algorithms. This is precisely why, until the last decade, magnetic bearings did not become a practical alternative to rolling element bearings. Today, magnetic bearing technology has become viable because of advances in microprocessing controllers that allow for con dent and robust active control [CJM04]. Magnetic bearings operate contactlessly and are therefore free of lubricant and wear. They are largely immune to heat, cold and aggressive substances and are operational in vacuum. Because of their low energy losses they are suited for applications with high rotation speeds. The forces act through an air gap, which allows magnetic suspension through hermetic encapsulations [Bet00].Tesi

Advances in the Field of Electrical Machines and Drives

Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications