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All Journal International Journal of Advances in Applied Sciences Perfecting a Video Game with Game Metrics Proceeding International Applied Business and Engineering Conference
Mohd Zaki Nuawi
Universiti Kebangsaan Malaysia
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Theory and development of magnetic flux leakage sensor for flaws detection: A review Nor Afandi Sharif; Rizauddin Ramli; Abdullah Zawawi Mohamed; Mohd Zaki Nuawi
International Journal of Advances in Applied Sciences Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (793.986 KB) | DOI: 10.11591/ijaas.v8.i3.pp208-216

Abstract

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.  
Spark plug failure detection using Z-freq and machine learning Nor Azazi Ngatiman; Mohd Zaki Nuawi; Azma Putra; Isa S. Qamber; Tole Sutikno; Mohd Hatta Jopri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.22027

Abstract

Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification.
Prediction of the Second Transition Point of Tool Wear Phase Using Vibratory Signal Analysis (ZROT) Nur Adilla Kasim; Mohd Ghafran Mohamed; Mohd Zaki Nuawi
International ABEC 2021: Proceeding International Applied Business and Engineering Conference 2021
Publisher : International ABEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1064.165 KB)

Abstract

Early intervention to change worn cutting tool before its failure could avoid unexpected machine downtime. A mathematical based predictive model is employed to estimate early tool failure using vibratory signal. The statistical based signal analysis technique as wear tracking analysis is applied in the predictive model to outline the data pattern concerning wear and number of cutting. The signal analysis based on the changes in the vibration signatures that captured from accelerometer during the milling operation throughout the tool life. A significant correlation between the tool flank wear and the statistical index has achieved. The tool life as a function of the acceleration amplitude of assimilated vibrations. Selected curve fitting equations are considered to decide the transition point between the steady state and failure region. The result shows a significant expectation of determining the second transition point with estimate value of 0.235mm below the rapid wear (<0.25mm). The accuracy, reliability and robustness of the predicted transition point were then parallel against another sensing elements where it predicts almost the same transition point. The de-termination of the second transition point will assist the preparation to anticipate the tool to be bro-ken. The results reflected that the model gives reasonable estimation of tool life and the transition points at which changes of the region transpire.
Co-Authors Abdullah Zawawi Mohamed Azma Putra Isa S. Qamber Mohd Ghafran Mohamed Mohd Hatta Jopri Nor Afandi Sharif Nor Azazi Ngatiman Nur Adilla Kasim Rizauddin Ramli Tole Sutikno