Diagnosis, classification and prognosis of rotating machine using artificial intelligence

The demand for cost efficient, reliable and safe rotating machinery requires accurate fault diagnosis, classification and prognosis systems. Therefore these issues have become of paramount important so that the potential failures of rotating machinery can be managed properly. Various methods have been applied to tackle these issues, but the accuracy of those methods is just satisfactory only. This research, therefore propose appropriate methods for fault diagnosis, classification and prognosis systems. For fault diagnosis and classification, the vibration data was obtained from Western Reserved University. The vibration signal was processed through pre-processing stage, features extraction, features selection before the developed diagnosis and classification model were built. For fault prognosis systems, the acoustic emission and vibration signals were used as input signals. Furthermore, ANN was used as prognosis systems of rotating machinery failure. The simulation results for fault diagnosis, classification and prognosis systems show that proposed methods perform very well and accurate. The proposed model can be used as tools for diagnosing rotating machinery failures

Electronic Abacus (e-Abacus) using FPGA Altera DE2 Board

The development of electronic abacus using Altera DE2 115, is to integrate the use of abacus along with electronic devices which offer better visualisation of the abacus operations. The main focus of this technology is to assist the primary school students in validating a fundamental arithmetic operation. The electronic abacus is developed by integrating an abacus, abacus decoder module and field programming gate array (FPGA) based processor.  DE2 115 is chosen as the development module and very high-speed integrated circuit hardware description language (VHDL) as a main programming language. The arithmetic algorithm developed for the electronic abacus is limited to the computational of whole numbers only, involving the basic arithmetic operation of additional, subtraction, multiplication and division. This electronic abacus comes with two operational modes, display and arithmetic mode. In the display mode, the abacus beads position at column one until column seven is displayed as numerical representation on the liquid crystal display (LCD) screen. A computation of arithmetic operations with less than three operators is available in the arithmetic mode with the capability of displaying the negative numerical and infinite value. From the simulation conducted in Quartus II, the implementation of the algorithm in FPGA utilise 4% from the total logical element allowed and consume approximately 143.4 mW of power. As a conclusion, this enhanced ancient apparatus hopefully will contribute to the development of more lively and interesting teaching approach.&nbsp

Development of artificial neural network based MPPT for photovoltaic system during shading condition

This paper presents Feedforward Neural network (FFNN) and Elman network controllers to control the maximum power point tracking (MPPT) of photovoltaic (PV). MPPT is a method used to extract the maximum available power from photovoltaic module by designs them to operate efficiently. Thus, cell temperatures and solar irradiances are two critical variable factors to determine PV output powers. The performances of the controller is analyzed in four conditions which are i) constant irradiation and temperature, ii) constant irradiation and variable temperature, iii) constant temperature and variable irradiation and iv) variable temperature and irradiation. The proposed systems are simulated by using MATLAB-SIMULINK. Based on the results, FFNN controller has shown the better performance compare to the Elman network controller during partial shading conditions

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

Psychological Resilience of Employees in Adversity Quotient: Malaysian Perspective in Facing Challenges

Psychological resilience is the ability to manage psychologically or emotionally with a catastrophe or to swiftly recover to pre-crisis position. Adversity is one of the most important abilities for psychological resilience. Workers in the twenty-first century must be able to deal with adversity in a challenging work environment, especially during a pandemic. The purpose of this study was to determine the level of adversity quotient among Malaysian employees during the pandemic period. Different employees' ability adversity quotients have also been compared in terms of gender, race, and age. This study's design is a cross-sectional survey with 585 respondents from all around Malaysia selected using a convenient sampling technique. A modified version of Dr. Paul G. Stoltz's Adversity Response Profile was employed as instrument in this study. The data was then analysed with the sum, median, mean, standard deviation, independent-sample t-test, and logistic regression. The findings of the study show that the majority of employees have a high adversity quotient score. In terms of gender, men employees tend to score higher than female employees. Employees aged 50 and over excel those aged 26 and below. However, the adversity quotient score for employees of different races is not significantly different. Employers should eventually take serious concern by implementing the appropriate intervention programme or policy aimed at potential employees in order to overcome their shortcoming in the adversity quotient and, more importantly, to enhance the employee's ability to face challenges in order to drive the company's mission and vision

FPGA based maximum power point tracking of photovoltaic system using perturb and observe method during shading condition

Nowadays, PV cell which is known as a photovoltaic is one of the most important parts in electrical field to convert photo light to voltage and current at the desired output voltage and frequency by using varies control techniques. This project presents design and implementation of FPGA Based Maximum Power Point Tracking (MPPT) Controller for Photovoltaic system using Perturb and Observed method (P&O). The MPPT controller is employed to control and get Maximum Power Point (MPP) from the source. Altera DE1 board is used as a controller for the implementation of the MPPT system. The simulation of this FPGA based MPPT controller is designed and implemented using Quartus II VHDL software tools. The results shown, the same signal obtained from Matlab simulink software as compared with Quartus II. It has been observed that the designed system has been successfully extracting the MPP during partially shading condition as in the simulations

Predicting remaining useful life of rotating machinery based artificial neural network

Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure

Prediction of classroom reverberation time using neural network

In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds

Determining the Articles Acceptance Using Logic of Fuzzy Inference System Tsukamoto

Publication of scientific article is now getting bigger, but the process of the scientific article acceptance takes a long time. The longest stage in the process, especially, is in the review process. The process about article assessment consists of many criteria which cause a very high level of subjectivity. Computerized system on the assessment of the scientific article acceptance apply a reasoning scheme using Fuzzy Inference Tsukamoto Logic; therefore, by using the logic, the duration issue in the assessment process can be handled fast

Development of PC based fuzzy logic controller for DC motor

This paper presents a development of PC Based Fuzzy Logic Controller of DC motor. Data Acquisition (DAQ) USB Card is use as interfacing hardware between PC and DC motor assist with LabVIEW software. Fuzzy Logic Controller (FLC) is designed as a controller to control DC motor movement on Flexible Robot Manipulator (FRM). In order to design modeling system of FRM, System Identification is implemented to produce the transfer function of a model which takes into account the FRM in order to control the vibration link to point out the accurate position. FLC has been selected as optimum controller because it gave better performance of vibration control through feedback signal of FRM. The result shows FLC gave a good performance, approximately 50% of reducing the vibration signal, which the link of FRM is encountered moves in smooth condition to the end point of link movement. To sum up, the proposed system using FLC is capable of reducing the vibration while maintaining the accurate point position of the link of FRM