In this paper, the data filtering based Recursive Least Squares algorithm (RLS) of linear Box–Jenkins systems is proposed for fault detection. The system is decomposed into two subsystems, one containing the parameters of the system model and the other containing the parameters of the noise model to improve the computational load. In addition, the parameters of the system model and the noise model are estimated and their accuracy are developed. The model validation is tested using two statistical methods, histogram and mean square errors. The residual is generated based on the proposed algorithm to design the threshold and therefore, this designed thresholdis used for fault detection. Simulation results are performed to illustrate the algorithm performance.

ABSTRACT

In present day triple tube heat exchanger is the most common type heat exchanger widely use in ventilation & air conditioning systems, oil refinery and other large chemical process, because it suits high pressure application. The process in solving simulation consists of modelling and meshing the basic geometry of triple tube heat exchanger using CFD package ANSYS 14.0. The objective of the project is design of triple tube heat exchanger and study the flow and temperature field inside the triple tube using ANSYS software tools. The heat exchanger contains 3 tubes and 500 mm length triple tube diameter 75 mm. In simulation will show how the flow pattern in the triple tube of the heat exchanger with heat transfer effects the efficiency due to the new design of the geometry of triple tube, which results in a significant increase in heat transfer coefficient per unit pressure drop in the heat exchanger spaced reduced

The main goal of the current paper is focused to investigate the effect of isothermal heat treatment

temperature and time on microstructure and strength in a medium carbon vanadium titanium free micro

alloyed steel. Isothermal heat treatment was carried out in the temperature range 350 to 600 C° at

different holding times varying from 2s to 1200s followed by water quenching. Samples were

investigated using optical microscope (OM) and scanning electron microscopy (SEM) paired with

energy dispersive spectroscopy (EDS) and by compressive testing using a servo-hydraulic testing

machine. The results show that, the final microstructure of samples held at high temperatures (550 and

600°C) consists of polygonal intra-granularly nucleated ferrite idiomorphs, combined with grain

boundary ferrite and pearlite were produced and followed by retained austenite that transformed to

martensite upon quenching (incomplete transformation phenomenon). At intermediate temperatures

(450 and 500 °C) an interlocked acicular ferrite (AF) microstructure is produced, hence acicular ferrite

becomes prevalent in the microstructure at (450 °C). The microstructure after the heat treatment at

500°C consists coarse nonpolygonal ferrite grains separated by pearlite colonies. However, at low

temperatures (400 and 350°C), the final microstructure of the samples held at 350°C consists of

bainitic sheaves, where the sheave of parallel acicular ferrite plates, similar to bainitic sheaves but

intra-granularly nucleated were observed, which called in some references as sheaf type acicular ferrite

for samples isothermally treated at 400°C. Yield stress was determined by compression testing on samples

with final Microstructure, the results show that, the observed change in the microstructure is related by a

marked decrease of compressive yield strength, approximately from 1000 to 700 MPa.

For Arabic letters recognition, we achieve three of pattern recognition approaches namely gray level co-occurrence matrix (GLCM), local binary pattern recognition (LBP) and artificial neural network (ANN) and compare between them to result best performance. Two of these methods level co-occurrence matrix and local binary pattern recognition are used for feature extraction whereas in artificial neural network (ANN) we use the intensity values of pixels for input of the neural network. Two classifiers are used, the K-Nearest Neighbor classifier (KNN) for the LBP, GLCM and neural network classifier for (ANN) artificial neural network. Also, we evaluate the results by using leave one person out approach, fold classification and leave one out.

this paper derives extended three-stage recursive identification algorithm of MISO for (CARARMA) systems. Based on The decomposition technique, four subsystems are obtained and the parameters of each subsystem are identified. Some model validation methods are computed to measure the model value and Akaike’s Final Prediction Error Criterion (FPE) is used to verify the selection of system order. The algorithm has a high computational efficiency because the covariance matrices dimensions become small in each subsystem. Finally, this algorithm effectiveness is demonstrated in simulation example.

Jamal Abdul-Nasser is a major trunk road that divides Sebha city into two semi-equal parts. It connects almost all districts with different activities and services in the center of the city. Alongside Jamal Abdul-Nasser Street, exist various types of activities: administrative, educational, commercial, public services and some other activities. Due to its importance, the street carries out the densest traffic movement within the city. This paper tries to study and analyze traffic movement at five intersections on Jamal Abdul-Nasser Street: (1) Al-Nahdha; (2) Khaled IbnAl-Walid; (3) Al-Khotoot; (4) Al-Tijari Bank; (5) Al-Zeraie Bank. Selection of the pre-mentioned intersections referred to their importance as converging points to various important activities and land uses. Data required for the study purposes include intersection characteristics, traffic flow characteristics and traffic composition pattern. The results reveal the significance of taking care of the geometric and regulatory aspects that enable the intersections to perform well with regard the smoothness of traffic flow.