Development of high-performance anode/electrolyte/cathode micro-tubular solid oxide fuel cell via phase inversion-based co-extrusion/ co-sintering technique

A complete set of triple-layer (anode/electrolyte/cathode) hollow fiber for high temperature micro-tubular solid oxide fuel cell (MT-SOFC) consisting of nickel oxide (NiO) – yttria-stabilized zirconia (YSZ)/YSZ/lanthanum strontium manganite (LSM) – YSZ has been successfully fabricated in this study. A simplified fabrication technique of phase inversion-based co-extrusion/co-sintering has yielded a perfectly bounded sandwich structure with free-delamination and defect layers. The effect of co-sintering temperatures (1300 °C–1450 °C) on the morphologies, elemental distributions, electrolyte gas-tightness, mechanical strength, electrochemical performance and the impedance spectra test are well-inspected. The increase of co-sintering temperature has significant effects on the anode finger-like micro-channels shrinkage where the voids become very sharp-thin structure; and developing a thin gas-tight electrolyte layer. Whereas, rapid co-sintering rate (10 °C min -­¹) and large particle size of 3–5 μm (micron) of YSZ has hindered the formation of fully dense cathode layer resulting from higher co-sintering temperature. Correspondingly, with only 0.1116 Ωcm2 value of area-specific resistance (ASR), a maximum power density has increased from 0.34 W cm ­² to 0.75 W cm ­² with 1.05 V OCV at 700 °C when the co-sintering temperature ranging from 1400 °C to 1450 °C; which comparable with single-layer counterpart

Blade fault diagnosis using artificial intelligence technique

Blade fault diagnosis is conventionally based on interpretation of vibration spectrum and wavelet map. These methods are however found to be difficult and subjective as it requires visual interpretation of chart and wavelet color map. To overcome this problem, important features for blade fault diagnosis in a multi row of rotor blade system was selected to develop a novel blade fault diagnosis method based on artificial intelligence techniques to reduce subjective interpretation. Three artificial neural network models were developed to detect blade fault, classify the type of blade fault, and locate the blade fault location. An experimental study was conducted to simulate different types of blade faults involving blade rubbing, loss of blade part, and twisted blade. Vibration signals for all blade fault conditions were measured with a sampling rate of 5 kHz under steady-state conditions at a constant rotating speed. Continuous wavelet transform was used to analyse the vibration signals and its results were used subsequently for feature extraction. Statistical features were extracted from the continuous wavelet coefficients of the rotor operating frequency and its corresponding blade passing frequencies. The extracted statistical features were grouped into three different feature sets. In addition, two new feature sets were proposed: blade statistical curve area and blade statistical summation. The effectiveness of the five different feature sets for blade fault detection, classification, and localisation was investigated. Classification results showed that the statistical features extracted from the operating frequency to be more effective for blade fault detection, classification, and localisation than the statistical features from blade passing frequencies. Feature sets of blade statistical curve area was found to be more effective for blade fault classification, while feature sets of blade statistical summation were more effective for blade fault localisation. The application of feature selection using genetic algorithm showed good accuracy performance with fewer features achieved. The neural network developed for blade fault detection, classification, and localisation achieved accuracy of 100%, 98.15% and 83.47% respectively. With the developed blade fault diagnosis methods, manual interpretation solely dependent on knowledge and the experience of individuals can be reduced. The novel methods can therefore be used as an alternative method for blade fault diagnosis

Evaluation of machine learning techniques for electro-mechanical system diagnosis

The application of intelligent algorithms, in electro-mechanical diagnosis systems, is increasing in order to reach high Reliability and performance ratios in critical and complex scenarios. In this context, different multidimensional intelligent diagnosis systems, based on different machine learning techniques, are presented and evaluated in an electro-mechanical actuator diagnosis scheme. The used diagnosis methodology includes the acquisition of different physical magnitudes from the system, such as machine vibrations and stator currents, to enhance the monitoring capabilities. The features calculation process is based on statistical time and frequency domains features, as well as timefrequency fault indicators. A features reduction stage is, additionally, included to compress the descriptive fault information in a reduced feature set. After, different classification algorithms such as Support Vector Machines, Neural Network, k-Nearest Neighbors and Classification Trees are implemented. Classification ratios over inputs corresponding to previously learnt classes, and generalization capabilities with inputs corresponding to learnt classes slightly modified are evaluated in an experimental test bench to analyze the suitability of each algorithm for this kind of application.Peer ReviewedPostprint (author’s final draft

Experimental study of the behavior of PEEK CF30 bearing to cyclical loading and variable rotational speed

Automotive components are most often being manufactured in thermoplastic polymers.Their use present several advantages such as reduced weight, high design flexibility and styling capabilities, good balance of properties (ductility, insulation, no corrosion), superior level of integration of functionalities, low processing costs. Rotating elements are widely used in many practical applications, irrelevant the thermal analysis is of utmost importance. This study evaluated the possibility of infrared thermograph to measure accurately the temperature of elements of a rotating device, particulary the PEEK CF30 and carbon steel F114 bearings, within the scope of condition monitoring. The tested machine was an electric motor that operated in multiples regimes. The thermograms were acquired by a fixed thermographic camera and were processed and recorded every 15 minutes. Both bearing materials presented similar behaviour

Radial basis function network based MPPT for photovoltaic system during shading condition

The output powers of photovoltaic (PV) system are crucially depending of the two variable factors, which are the cell temperatures and solar irradiances. A method to utilize effectively the PV is known as a maximum power point tracking (MPPT) method. This method is extract the maximum available power from PV module by making them operates at the most efficient output. This paper presents Radial Basis Function (RBF) Network to control the MPPT of PV system. The performances of the controller is analyzed in four conditions with are constant irradiation and temperature, constant irradiation and variable temperature, constant temperature and variable irradiation, and variable temperature and irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, RBF controller has shown better performance during partially shaded conditions

Integración de inteligencia en la MIB del Modelo OSI para la gestión de redes de telecomunicaciones

La Gestión de red se define como el conjunto de actividades dedicadas al control y vigilancia de los recursos existentes en las redes de telecomunicaciones. En los complejos sistemas actuales, es necesario realizar una gestión de la red asistida por un software avanzado. La Inteligencia Artificial se incorpora a la gestión de las redes, con el fin de facilitar labores de administración y control de toda la información que proviene de los recursos gestionados, dando origen a la Gestión Inteligente de las Redes. Este nuevo paradigma, proporciona a los sistemas de gestión de un mayor grado de cohesión con las tecnologías de comunicaciones actuales, a la vez de disponer de todas las posibilidades y ventajas aportadas por la Inteligencia Artificial. Nuestro estudio tiene como objetivo perfeccionar las técnicas actuales de gestión. Para ello se establecen mecanismos que permiten una mayor correlación entre las especificaciones de la red y las aplicaciones que efectúan el tratamiento de la información de gestión. Presentamos una nueva concepción denominada “Gestión Inteligente Integrada” y una extensión del modelo de gestión OSI, que contempla la inclusión del conocimiento de gestión, en las propias especificaciones de los objetos gestionados. Este modelo consigue reunir conceptos que actualmente pertenecen a distintos ámbitos de estudio, la Inteligencia Artificial y la Información de Gestión del sistema. De esta forma se obtiene una solución global, que permite a los administradores de redes utilizar la potencia aportada por la Inteligencia Artificial, en particular de los Sistemas Expertos, de una forma sencilla y transparente

A Machine Learning Approach for Tracking the Torque Losses in Internal Gear Pump - AC Motor Units

This paper deals with the application of speed variable pumps in industrial hydraulic systems. The benefit of the natural feedback of the load torque is investigated for the issue of condition monitoring as the development of losses can be taken as evidence of faults. A new approach is proposed to improve the fault detection capabilities by tracking the changes via machine learning techniques. The presented algorithm is an art of adaptive modeling of the torque balance over a range of steady operation in fault free behavior. The aim thereby is to form a numeric reference with acceptable accuracy of the unit used in particular, taking into consideration the manufacturing tolerances and other operation conditions differences. The learned model gives baseline for identification of major possible abnormalities and offers a fundament for fault isolation by continuously estimating and analyzing the deviations