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Articles by M.N. Baharin
Total Records ( 2 ) for M.N. Baharin
  Z.M. Nopiah , M.N. Baharin , S. Abdullah and M.I. Khairir
  Abrupt changes are changes that occur at a time instant at which properties suddenly change, but before and after which properties are constant in some sense. The detection of abrupt changes refers to the determination whether, such a change occurred in the characteristics of the considered subject. Running Damage Extraction (RDE) method is a new technique that was developed based on the fatigue damage calculation in detecting the abrupt changes. The objective of this study was to observe the capability of RDE method in analyzing fatigue data for detection of abrupt changes. For the purpose of this study, a collection of nonstationary data that exhibits a random behavior was used. This random data was measured in the unit of microstrain on the lower suspension arm of a car. Experimentally, the data was collected for 60 sec at a sampling rate of 500 Hz, which gave 30,000 discrete data points. By using RDE algorithm, a running damage plot was constructed in monitoring the damage changes for fatigue data. Global signal statistical value indicated that the data were non Gaussian distribution in nature. The result of the study indicates that RDE technique is applicable in detecting the abrupt changes that exist in fatigue time series data by isolating the high and low amplitude event into different segmentation.
  A. Lennie , S. Abdullah , Z.M. Nopiah and M.N. Baharin
  This study presents an analysis on variable amplitude loading strains data by using amplitude probability distribution function, power spectral density function and cross correlation function techniques. The objectives of this study are to observe the capability of these techniques in investigating the time series behaviour in terms of distribution and statistical values and also detecting the similarity of pattern signal. In this study, the data consisting of non-stationary variable amplitude loading strains data exhibiting a random behaviour was used as a set of case study. This random data was collected on the lower suspension arm of an automobile component travelling on pavé and highway route. The data was repetitively measured for 60 sec at the sampling rate of 500 Hz, which provided 30,000 discrete data points. The collected data was then calculated and analysed for the signal distribution, statistics parameter and cross correlation values. Higher calculated cross correlation values were then selected to analyse fatigue damage prediction. From amplitude probability distribution function and power spectral density function diagrams, the result can be concluded that the non-Gaussian distribution can be related to a broad band signal, while for Gaussian distribution for a narrow band signal. The findings from this study are expected to be used in determining the pattern behavior that exists in VA signals.
 
 
 
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