Intelligent Fault Diagnosis Model for Rotating Machinery Based on Fusion of Sound Signals



Published Nov 11, 2020
M. Saimurugan R. Nithesh


The failure of rotating machine elements causes unnecessary downtime of the machine. Fault in the rotating machinery can be identified from noises, vibration signals obtained from sensors. Bearing and shaft are the most important basic rotating machine elements. Detection of fault from vibration signals is widely used method in condition monitoring techniques for diagnosis of machine elements. Fault diagnosis from sound signals is cost effective than vibration signals. Sound signal analysis is not well explored in the field of automated fault diagnosis. Under various simulated fault conditions, the sound signals are obtained by placing microphone near the bearing for different speeds. The features are extracted by using statistical and histogram methods. The best features of sound signals are obtained by decision tree algorithm. The extracted features are used as inputs to the classifier-Artificial Neural Network. The classification accuracy results from statistical and histogram features are obtained and compared.

Abstract 166 | PDF Downloads 158



fault diagnosis, Neural Networks, data mining, data fusion, SOUND SIGNALS

Vyas, N.S., & Satishkumar, D. (2001) Artificial neural network design for fault identification in a rotor-bearing system, Mechanism and Machine Theory, 36 157±175.
Randall, Robert Bond. (2011), Vibration-based condition monitoring - Industrial, aerospace and automotive applications. UK: John Wiley and Sons, Ltd., Publication.
Samanta, B., & Al-Balushi, K.R. (2003). Artificial neural network based fault diagnostics of rolling element bearings using time domain features, Mechanical Systems and Signal Processing 17 (2), 317–328.
Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan, Kaufmann.
Sugumaran,V., & Ramachandran,K.I. (2011). Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning, Expert Systems with Applications 38 ,4901–4907.
Samadani, M., Kitio Kwuimy, C.A., & Nataraj, C. (2015). Model-based fault diagnostics of nonlinear systems using the features of the phase space response. Communications in Nonlinear Science and Numerical Simulation 20.2: 583-593.
Zhiqiang Chen, Chuan Li & René Vinicio Sánchez. (2015). Multi-layer neural network with deep belief network for gearbox fault diagnosis. Journal of Vibroengineering. Vol. 17, Issue 5, p. 2379-2392.
Chuan Li, Ming Liang, Tianyang Wang. (2015). Criterion fusion for spectral segmentation and its application to optimal demodulation of bearing vibration signals, Mechanical Systems and Signal Processing 64-65, 132–148.
Heng, R. B. W., & Nor, M. J. M. (1998). Statistical Analysis of Sound and Vibration Signals for Monitoring Rolling Element Bearing Condition. Applied Acoustics, Vol. 53, No. 1-3, pp. 21 l-226.
Alaa Abdulhady Jaber & Robert Bicker. (2016). Fault Diagnosis of Industrial Robot Bearings Based on Discrete Wavelet Transform and Artificial Neural Network. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 017.
Amarnath, M., Sugumaran, V., Hemantha Kumar. (2013). Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement 46, 1250–1256.
Vapnik, V.N., (1999). An overview of statistical learning theory. IEEE transactions on Neural Networks 10, 988–1000.
Fonseca, D.J., Navaresse, D.O., & Moynihan, G.P. (2003). Simulation Meta modeling through artificial neural networks. Engineering Applications of Artificial Intelligence 16, 177–183.
Saimurugan, M., Ramachandran, K.I., Sugumaran, V., & Sakthivel, N.R. (2011). Multi- component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Systems with Applications 38, 3819–3826.
Smalley, A. J., Baldwin, R. M., Mauney, D. A., & Millwater, H. R. (1996). Towards risk based criteria for rotor vibration. International proceedings of the institute of mechanical engineers—Vibrations in rotating machinery (pp. 517–527).
Kankar, P.K., Sharma Satish, C., & Harsha, S.P. (2011). Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications 38, 1876–1886.
Widodo, A., & Yang, B.S. (2007). Review on support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21, 2560–2574.
Yuan, S., & Chu, F. (2007). Fault diagnosis based on support vector machines with parameter optimization by artificial immunization algorithm. Mechanical Systems and Signal Processing, 21, 1318–1330.
Meirovitch, Leonard. (1986). Elements of Vibration analysis. USA: McGraw-Hill Publication.
Caudill, M., & Butler, C. (1992). Understanding Neural Networks: Computer Explorations, MIT Press, Cambridge, MA.
Salzberg, S.L. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach. Data mining and knowledge discovery, 1(3), pp.317-328.
Hertz, J., Krogh & Palmer, R.G. (1991). Introduction to the Theory of Neural Computation. Addison Wesley. California.
Chow, S.K.H., Lee, E.W.M., Li, D.H.W. (2012). Short-term prediction of photovoltaic energy generation by intelligent approach, Energy and Buildings 55, 660-667.
Xin Lei & Peter A. Sandborn. (2016). PHM-Based Wind Turbine Maintenance Optimization Using Real Options. International Journal of Prognostics and Health Management, ISSN 2153-2648, 2016 008.
Technical Papers