Papers by Author: Mohd Noor Baharin

Abstract: This paper presents the comparative study on two types of the clustering technique for decomposing Variable Amplitude (VA) loadings signals based on its amplitude. These two techniques are used to recognize clusters or patterns of fatigue damaging events in the record which will bring aboutthe majority of fatigue damage. However, one of the problems that existswhencomparing which technique will produce better clusters is the fact thata clustering validation index isneeded. In this study, techniques that were used were theFuzzy C-means and C-means. At first, the VA data weresegmented using the Running Damage Extraction (RDE) technique. Then, each segment produced wasanalysed using the strain life approach and global statistical signal values. Finally, the accuracy of each clustering technique wasmeasured based on the OV coefficient index. From the study, the index shows that the Fuzzy C-means technique produced much better clusters rather than the C-mean clustering technique.

Abstract: This paper presents a relative study on variable amplitude (VA) strain data distribution using the approach of probability density function (PDF) and power spectral density (PSD). PDF is a technique to identify the probability of the value falling within a particular interval, and a PSD is to measure the power of a signal by converting it from the time domain to the frequency domain. The objective of this study is to observe the applicability of both techniques in detecting the pattern behaviour in terms of energy and probability distribution. For this reason, a set of case study data consist of nonstationary VA pattern with a random behaviour was used. This kind of data was measured by fixing a strain gauge that connected to the strain data acquisition on the lower suspension arm of a mid-sized sedan car. The data was measured for 60 seconds at the sampling rate of 500 Hz, which gave 30,000 discrete points. The distribution of collected data was then calculated and analysed in the form of both PDF and PSD, and they were then compared for further analysis. The findings from this study are expected for determining the pattern behaviour that exists in VA strain signals.

Abstract: This paper presents the optimisation of real-time performance of the genetic algorithm clustering method. This performance optimisation concerns the population diversity and limitation and is based on actual runtime of the algorithm. A real-time ticker is incorporated into the algorithm for actual runtime measurement. For population diversity and limitation, a controlled k-means analysis is performed on the population of solutions to determine its diversity. Achieving a less diverse population in less amount of time without sacrificing the accuracy of the algorithm will help reduce the time-complexity of the algorithm, thus opening up the potential for the algorithm to cluster data in higher dimensions. Results from this study will be used for improving the method of clustering fatigue damage features of automotive components using genetic algorithm based methods.

Abstract: This paper presents the comparative study on three types of fatigue data editing technique for summarising long records of fatigue data. Two of techniques were developed based on timefrequency domain (continous wavelet and discrete wavelet) and another one technique was developed based on time domain. These three techniques are used to extract fatigue damaging events in the record that cause the majority of fatigue damage, whilst preserving the load cycle sequence in the data. The objective of this study is to observe the capability of each technique in summarising long records of fatigue data. For the purpose of this study, two set of nonstationary data that exhibits random behaviour was used. This random data was measured in microstrain unit on the SAE1045 material that were used as a lower suspension arm of a car. Experimentally, the data was collected for 60 seconds at sampling rate of 500Hz, which gave 30, 000 discrete data points. The result of the study indicates that all techniques are applicable in detecting and extracts fatigue damaging events that exist in the fatigue data. However, it was found that continous wavelet can extract the data better than the other technique based the shorten signals, retention of damage and statistical parameter.