Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review


  • Yasir Hassan Ali Department of Applied Mechanics and Design, Faculty of Mechanical Engineering, University Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Roslan Abd Rahman Department of Applied Mechanics and Design, Faculty of Mechanical Engineering, University Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Raja Ishak Raja Hamzah Department of Applied Mechanics and Design, Faculty of Mechanical Engineering, University Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia



Artificial intelligence method, acoustic emission, condition monitoring, fault diagnosis


Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.


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How to Cite

Ali, Y. H., Abd Rahman, R., & Raja Hamzah, R. I. (2014). Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review. Jurnal Teknologi, 69(2).