Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review
Keywords:Artificial intelligence method, acoustic emission, condition monitoring, fault diagnosis
AbstractAcoustic 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.
Mba, D. and R. B. Rao. 2006. Development of Acoustic Emission Technology for Condition Monitoring and Diagnosis of Rotating Machines. Bearings, Pumps, Gearboxes, Engines and Rotating Structures.
Tandon, N. and A. Choudhury. 1999. A Review of Vibration and Acoustic Measurement Methods for the Detection of Defects in Rolling Element Bearings. Tribology international. 32(8): 469â€“480.
Al-Ghamd, A. M. and D. Mba. 2006. A Comparative Experimental Study on the Use of Acoustic Emission and Vibration Analysis for Bearing Defect Identification and Estimation of Defect Size. Mechanical Systems and Signal Processing. 20(7): 1537â€“1571.
Filippetti, F., G. Franceschini and C. Tassoni. 1996. A Survey of AI Techniques Approach for Induction Machine On-line Diagnosis. Proceedings of Power Electronics and Motion Control PEMC. 2: 314â€“318.
Siddique, A., G. Yadava and B. Singh. 2003. Applications of Artificial Intelligence Techniques for Induction Machine Stator Fault Diagnostics: Review. Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003. 4th IEEE International Symposium on. 29â€“34.
J., V. S. 1999. Acoustic Emission: Standards And Technology Update Philadelphia. USA: West Conshohocken: [ASTM] American Society for Testing and Materials. 1st Edn.
Gao, L., F. Zai, S. Su, H. Wang, P. Chen and L. Liu. 2011. Study and Application of Acoustic Emission Testing in Fault Diagnosis of Low-speed Heavy-duty Gears. Sensors. 11(1): 599â€“611.
Rubioa, E., R. Tetib and I. Baciub. 2011. Advanced Signal Processing in Acoustic Emission Monitoring Systems for Machining Technology. Intelligent Production Machines and Systems-2nd I* PROMS Virtual International Conference 3-14 July 2006: 1.
Nivesrangsan, P., J. Steel and R. Reuben. 2007. Acoustic Emission Mapping of Diesel Engines for Spatially Located Time Seriesâ€”Part II: Spatial Reconstitution. Mechanical Systems and Signal Processing. 21(2): 1084â€“1102.
Nivesrangsan, P., J. Steel and R. Reuben. 2007. Source Location of Acoustic Emission in Diesel Engines. Mechanical Systems and Signal Processing. 21(2): 1103â€“1114.
Steel, J. and R. Reuben. 2005. Recent Developments in Monitoring of Engines Using Acoustic Emission. The Journal of Strain Analysis for Engineering Design. 40(1): 45â€“57.
Albarbar, A., F. Gu and A. Ball. 2010. Diesel Engine Fuel Injection Monitoring Using Acoustic Measurements and Independent Component Analysis. Measurement. 43(10): 1376â€“1386.
El-Ghamry, M., R. Reuben and J. Steel. 2003. The Development of Automated Pattern Recognition and Statistical Feature Isolation Techniques for the Diagnosis of Reciprocating Machinery Faults Using Acoustic Emission. Mechanical Systems and Signal Processing. 17(4): 805â€“823.
Muravin, B. 2009. Acoustic Emission Science and Technoolgy. Journal of Building and Infrastructure Engineering of the Israeli Association of Engineers and Architects. Israel.
Kok, J. N., E. J. Boers, W. A. Kosters, P. van der Putten and M. Poel. 2009. Artificial Intelligence: Definition, Trends, Techniques, and Cases. Artificial intelligence.
Pham, D. and P. Pham. 1999. Artificial Intelligence in Engineering. International Journal of Machine Tools and Manufacture. 39(6): 937â€“949.
Saxena, A. and A. Saad. 2006. Genetic Algorithms for Artificial Neural Net-based Condition Monitoring System Design for Rotating Mechanical Systems. Applied Soft Computing Technologies: The Challenge of Complexity. Springer. 135â€“149.
Aguiar, P. R., C. H. Martins, M. Marchi and E. C. Bianchi. 2012. Digital Signal Processing for Acoustic Emission.
Kouroussis, D., A. Anastassopoulos, J. LENAIN and A. PROUST. 2001. Advances in Classification of Acoustic Emission Sources. Les JournÃ©es COFREND, Reims.
Al-Balushi, K. and B. Samanta. 2000. Gear Fault Diagnostics Using Wavelets and Artificial Neural Network. COMADEM 2000: 13 th International Congress on Condition Monitoring and Diagnostic Engineering Management: 1001â€“1010.
Mahamad, A. K. B. 2010. Diagnosis, Classification and Prognosis of Rotating Machine using Artificial Intelligence. Kumamoto University.
Abu-Mahfouz, I. 2001. Condition Monitoring of a Gear Box Using Vibration and Acoustic Emission Based Artificial Neural Network. SAE Transactions. 110(6): 1771â€“1781.
Menon, S., J. N. Schoess, R. Hamza and D. Busch. 2000. Wavelet-based Acoustic Emission Detection Method with Adaptive Thresholding. SPIE's 7th Annual International Symposium on Smart Structures and Materials. 71â€“77.
Blahacek, M., M. Chlada and Z. PrevorovskÃ½. 2006. Acoustic Emission Source Location Based on Signal Features. Advanced Materials Research. 13: 77â€“82.
Fog, T. L., E. Brown, H. Hansen, L. Madsen, P. SÃ¸rensen, E. Hansen, J. Steel, R. Reuben and P. Pedersen. Exhaust Valve leakage Detection in Large Marine Diesel Engines. COMADEMÂ´ 98, 11th Int. Conf. on Condition Monitoring and Diagnostic Engineering Management: 269â€“279.
Kouroussis, D., A. Anastassopoulos, P. Vionis and V. Kolovos. 2000. Unsupervised Pattern Recognition of Acoustic Emission from Full Scale Testing of a Wind Turbine Blade. Journal of Acoustic Emission(USA). 18: 217.
Wang, J.-Z., L.-S. Wang, G.-f. Li and G.-H. Zhou. 2005. Prediction of Surface Roughness in Cylindrical Traverse Grinding Based on ALS Algorithm. Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. 1: 549â€“554.
Wang, Z., P. Willett, P. R. DeAguiar and J. Webster. 2001. Neural Network Detection of Grinding Burn from Acoustic Emission. International Journal of Machine Tools and Manufacture. 41(2): 283â€“309.
Dotto, F. R., P. R. d. Aguiar, E. C. Bianchi, P. J. Serni and R. Thomazella. 2006. Automatic System for Thermal Damage Detection in Manufacturing Process with Internet Monitoring. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 28(2): 153â€“160.
Kwak, J.-S. and M.-K. Ha. 2004. Neural Network Approach for Diagnosis of Grinding Operation by Acoustic Emission and Power Signals. Journal of Materials Processing Technology. 147(1): 65â€“71.
Aguiar, P., T. FranÃ§a and E. Bianchi. 2006. Roughness and Roundness Prediction in Grinding. Proceedings of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME 06). 25â€“28.
Aguiar, P. R., C. E. Cruz, W. C. Paula and E. C. Bianchi. Predicting Surface Roughness in Grinding using Neural Networks.
Goebel, K. and P. K. Wright. 1993. Monitoring and Diagnosing Manufacturing Processes Using a Hybrid Architecture with Neural Networks and Fuzzy Logic. EUFIT, Proceedings. 2.
Walker, J. L., S. S. Russell, G. L. Workman and E. V. Hill. 1997. Neural Network/Acoustic Emission Burst Pressure Prediction for Impact Damaged Composite Pressure Vessels. Materials Evaluation. 55(8): 903â€“907.
Shen, G., Q. Duan, Y. Zhou, B. Li, Q. Liu, C. Li and S. Jiang. 2001. Investigation of Artificial Neural Network Pattern Recognition of Acoustic Emission Signals for Pressure Vessels. NDT. 23: 144â€“146.
MacÃas, E. J., A. S. Roca, H. C. Fals, J. B. FernÃ¡ndez and J. C. Muro. 2013. Neural Networks and Acoustic Emission for Modelling and Characterization of the Friction Stir Welding Process. Revista Iberoamericana de AutomÃ¡tica e InformÃ¡tica Industrial RIAI. 10(4): 434â€“440.
Tian, Y., P. Lewin, A. Davies, S. Sutton and S. Swingler. 2002. Application of Acoustic Emission Techniques and Artificial Neural Networks to Partial Discharge Classification. Electrical Insulation, 2002. Conference Record of the 2002 IEEE International Symposium on. 119â€“123.
Szyszko, S. and P. Payne. 1991. Artificial Neural Networks for Feature Extraction from Acoustic Emission Signals. Measurements, Modelling and Imaging for Non-Destructive Testing, IEE Colloquium on. 6/1-6/6.
Ming, Z. X. 2006. Application of Acoustic Emission Technique in Fault Diagnostics of Rolling Bearing. Master's thesis. Tsinghua University, Beijing, Haidian.
Sibil, A., N. Godin, M. Râ€™Mili, E. Maillet and G. Fantozzi. 2012. Optimization of Acoustic Emission Data Clustering by a Genetic Algorithm Method. Journal of Nondestructive Evaluation. 31(2): 169â€“180.
Zadeh, L. A. 1965. Fuzzy sets. Information and control. 8(3): 338â€“353.
Zadeh, L. A. 1973. Outline of a New Approach to the Analysis of Complex Systems and Decision Processes. Systems, Man and Cybernetics. IEEE Transactions on. (1): 28â€“44.
Zadeh, L. A. 1968. Fuzzy Aalgorithms. Information and Control. 12(2): 94â€“102.
Hellmann, M. Fuzzy Logic Introduction. A Laboratoire Antennes Radar Telecom. FRE CNRS. 2272.
Cusido, J., M. Delgado, L. Navarro, V. Sala and L. Romeral. 2010. EMA Fault Detection Using Fuzzy Inference Tools. AUTOTESTCON, 2010 IEEE: 1â€“6.
Omkar, S., S. Suresh, T. Raghavendra and V. Mani. 2002. Acoustic Emission Signal Classification Using Fuzzy C-Means Clustering. Neural Information Processing, 2002. ICONIP'02. Proceedings of the 9th International Conference on. 4: 1827â€“1831.
de Aguiar, P. R., E. C. Bianchi and R. C. Canarim. Monitoring of Grinding Burn by Acoustic Emission.
Ren, Q., L. Baron and M. Balazinski. 2012. Fuzzy Identification of Cutting Acoustic Emission with Extended Subtractive Cluster Analysis. Nonlinear Dynamics. 67(4): 2599â€“2608.
Ren, Q., L. Baron and M. Balazinski. 2009. Application of Type-2 Fuzzy Estimation on Uncertainty in Machining: An Approach on Acoustic Emission During Turning Process. Fuzzy Information Processing Society, 2009. NAFIPS 2009. Annual Meeting of the North American. 1â€“6.
Ren, Q., L. Baron and M. Balazinski. 2011. Type-2 Fuzzy Modeling for Acoustic Emission Signal in Precision Manufacturing. Modelling and Simulation in Engineering. 2011: 17.
Ren, Q., M. Balazinski, L. Baron, K. Jemielniak, R. Botez and S. Achiche. 2014. Type-2 Fuzzy Tool Condition Monitoring System Based on Acoustic Emission in Micromilling. Information Sciences. 255: 121â€“134.
Ren, Q., L. Baron, M. Balazinski and K. Jemielniak. 2010. Acoustic Emission Signal Feature Analysis Using Type-2 Fuzzy Logic System. Fuzzy Information Processing Society (NAFIPS)/ 2010 Annual Meeting of the North American. 1â€“6.
Blahacek, M., Z. Prevorovsky, J. Krofta and M. Chlada. 2000. Neural Network Localization of Noisy AE Events in Dispersive Media. Journal of Acoustic Emission(USA). 18: 279.
Khalifa, S. and M. H. Komarizadeh. 2012. An Intelligent Approach Based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Walnut Sorting. Australian Journal of Crop Science. 6(2).
Vapnik, V. 1998. Statistical Learning Theory New York. NY: Wiley.
Burges, C. J. 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 2(2): 121â€“167.
Saravanan, N., V. Kumar Siddabattuni and K. Ramachandran. 2008. A Comparative Study on Classification of Features by SVM and PSVM Extracted Using Morlet Wavelet for Fault Diagnosis of Spur Bevel Gear Box. Expert Systems with Applications. 35(3): 1351â€“1366.
Widodo, A., B.-S. Yang, E. Y. Kim, A. C. Tan and J. Mathew. 2009. Fault Diagnosis of Low Speed Bearing Based on Acoustic Emission Signal and Multi-class Relevance Vector Machine. Nondestructive Testing and Evaluation. 24(4): 313â€“328.
Widodo, A., E. Y. Kim, J.-D. Son, B.-S. Yang, A. C. Tan, D.-S. Gu, B.-K. Choi and J. Mathew. 2009. Fault Diagnosis of Low Speed Bearing Based on Relevance Vector Machine and Support Vector Machine. Expert Systems with Applications. 36(3): 7252â€“7261.
Yu, Y. and L. Zhou. 2012. Acoustic Emission Signal Classification Based on Support Vector Machine. TELKOMNIKA Indonesian Journal of Electrical Engineering. 10(5): 1027â€“1032.
Chu-Shu, K. 2010. A Machine Learning Approach for Locating Acoustic Emission. EURASIP Journal on Advances in Signal Processing. 2010.
Yang, Z. and Z. Yu. 2012. Grinding Wheel Wear Monitoring Based on Wavelet Analysis and Support Vector Machine. The International Journal of Advanced Manufacturing Technology. 62(1â€“4): 107â€“121.
Yu, Y. and L. Zhou. 2010. Acoustic Emission Signal Classification Based on Support Vector Machine. Computer Engineering and Technology (ICCET), 2010 2nd International Conference, 16-18 April Chengdu. 6: 300â€“304.
How to Cite
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.