Janjarassuk, Udom

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Janjarassuk, Udom
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Janjarassuk, Udom
Janjarassuk, U.
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udom.ja@kmitl.ac.th

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    A comparative study of mixed-integer linear programming and genetic algorithms for solving binary problems
    (2018-06-15)
    Kuendee, Punyisa
    ;
    Janjarassuk, Udom
    This paper aims to investigate the capability of mixed-integer linear programming (MILP) method and genetic algorithm (GA) to solve binary problem (BP). A comparative study on the MILP method and GA with default and tuned setting to find out an optimal solution is presented. The mixed-integer programming library (MIPLIB 2010) is used to test and evaluate algorithms. The evaluation is shown in quality of the solution and the execution time of computation. The results show that GA is superior to MILP in execution time with inconsistent results. However, MILP is superior to GA in quality of the solution with more stable results.
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    Classification of bearing fault location and severity using cascade ANNs with statistical and spectral features
    (2019-01-01)
    Kuendee, Punyisa
    ;
    Janjarassuk, Udom
    Advance in machine learning techniques for machine condition monitoring is now an active field of interest in modern industry for predicting the future health of machines. In this paper, Artificial Neural Networks (ANNs) technique is investigated by using the existing datasets of ball bearing fault to classify the fault location and severity. To enhance the accuracy, the utilization of statistical features and spectral features are used in pre-processing with the proposed method of cascade ANNs. Motor vibration data is collected by using accelerometers attached to the housing in the drive end and fan end. To diagnose the faults in the rolling bearing, the vibration data is pre-processing by using Fast Fourier Transform (FFT) and other statistical features such as standard deviation, skewness and kurtosis etc. Then the feature data is fed into cascade ANN_1 and ANN_2 to classify the fault location and severity. The objective of this research is to solve the problem of poor features such as slippage frequency component in FFT response and inappropriate statistical feature datasets to help increase in accuracy and efficiency. The results show that the proposed method gives more accuracy when the poor features are existed and guarantee the accuracy near to 100%.