Sharifah Saon

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.

Implementation of Field Programmable Gate Array based Maximum Power Point Tracking controller of photovoltaic system

Photovoltaic (PV) cell is one of the electrical part to convert photo-light into electricity in order to generate the electrical power. This paper presents the implementation of Field Programmable Gate Array (FPGA) based Maximum Power Point Tracking (MPPT) controller in evaluating the Maximum Power Point (MPP) output voltage of PV system. Matlab Simulink and Quartus II VHDL software tools are used as simulator of this FPGA, while Altera DE2 board is used as a controller. Both simulator and hardware had shown the same results, which prove that the designed system has been successfully extracting the MPP. The system has been evaluated in sunny day and partially shaded conditions to analyze the respective outputs.

Comparison between GLCM and modified Zernike moments for material surfaces identification from photo images

Types of materials are one of an important data for research in acoustic engineering. This paper compares methods for extracting texture data of material surfaces for classification. Gray Level Co-occurrence Matrix (GLCM) and modified Zernike moments that is applied for image extraction are tested and compared with back propagation neural network used for classification. These methods are also applied to the Brodatz texture database as a general comparison. The GLCM method shows a good performance and regression, R>0.9 for the Brodatz database while the collected surfaces datasets using GLCM and modified Zernike moments as well as the Brodatz datasets using modified Zernike moments method had only managed an acceptable performance and regression of R>0.8.

A Simplified Malaysian Vehicle Plate Number Recognition

This paper propose an automatic inspection system of alphabets and numbers to recognize Malaysian vehicles plate number based on digital image processing and Optical Character Recognition (OCR). An intelligent OCR Training Interface has been used as a library and the system has been developed using LabVIEW Software. This software then is used to test with different situation to ensure the proposed system can be applied for real implementation. Based on the results, the proposed system shows good performance for inspection and can recognize an alphabets and numbers of vehicle plate number. To sum up, the proposed system can recognize the alphabets and numbers of the Malaysian vehicles plate number for inspection.

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 < 0.005 and R > 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

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.