PSI - Issue 14

Vamsi Inturi et al. / Procedia Structural Integrity 14 (2019) 937–944 Vamsi, Sabareesh, Vaibhav/ Structural Integrity Procedia 00 (2018) 000–000

944

8

of 48:1.Two CM techniques, namely, vibration analysis and acoustic signal analysis are integrated and the response is recorded. Two local gear faults such as tooth chip and tooth root crack are seeded with two different severity levels. EMD analysis is implemented and the statistical features are extracted from IMFs. Among the extracted features, most significant features are selected by decision tree and the SVM algorithm is applied to classify the features among the gear conditions (fault severity levels). Since, the classification accuracies of microphones are higher than the accelerometers, it can be concluded that acoustic signal analysis has better fault detection capabilities than vibration analysis. Hence, acoustic signal analysis can be exploited to detect the local gear faults of a wind turbine gearbox. References Uma Maheswari, R., Umamaheswari, R., 2017. Trends in non-stationary signal processing techniques applied to vibration analysis of wind turbine drive train – A contemporary review, Mechanical Systems and Signal Processing 85, 296-311. Rajesh Kumar., Manpreet Singh., 2013. Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal, Measurement 46, 537-545. Matej Zvokelj., Samo Zupan., Ivan Prebil., 2011. Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method, Mechanical Systems and Signal Processing 25, 2631-2653. Nizar Ahmed., Yogesh Pandya., Anand Parey., 2014. Spur gear tooth root crack detection using time synchronous averaging under fluctuating speed, Measurement 52, 1-11. Hui Li., Yuping Zhang., Haiqi Zheng., 2011. Application of Hermitian wavelet to crack fault detection in gearbox, Mechanical Systems and Signal Processing 25, 1353-1363. Sailendu Biswal., Jithin Donny George., Sabareesh, G,R., 2016. Fault size estimation using vibration signatures in a wind turbine test-rig, Procedia Engineering 144, 305-311. Jaouher Ben Ali., Nader Fnaiech., Lotfi Saidi., Brigitte Chebel Morello., Farhat Fnaiech., 2015. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals, Applied Acoustics 89, 16-27. Bayder, N., Ball, A., 2003. Detection of gear failures via vibration and acoustic signals using wavelet transform, Mechanical Systems and Signal Processing 17, 787-804. Amarnath, M., Praveen Krishna, I,R., 2011. Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings, Measurement and Technology 6, 279-287. Amarnath, M., Praveen Krishna, I,R., 2014. Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis, Measurement 58, 154-164. Peng, Z., Kessissoglou, N., 2003. An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis, Wear 255, 1221-1232. Adrian, D, Nembhard., Jyoti, K, Sinha., Andrew, J, Pinkerton., Keri Elbhbah., 2014. Combined vibration and thermal analysis for the condition monitoring of rotating machinery, Structural Health Monitoring 13, 281-295. Loutas, T,H., Roulias, D., Pauly, E., Kotopoulos, V., 2011. The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery, Mechanical Systems and Signal Processing 25, 1339-1352. Xiaoyuan Zhang., Jianzhong Zhou., 2013. Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines, Mechanical Systems and Signal Processing 41, 127-140. Norden, E.Huang., Zheng Shen., Steven, R.Long., Mangli, C.Wu., Hsing, H.Shih., Quanan Zheng., Nai Chyuan Yen., Chi Chao Tung., Henry, H.Liu., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society London A 454, 903-995. Yaguo Lei., Jing Lin., Zhengjia He., Ming, J, Zuo., 2013. A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing 35, 108-126. Sujatha, C., 2010. “ Vibration and Acoustics: Measurement and Signal Analysis ”, Tata McGraw Hill Education Private Limited, India, pp. 260. Sugumaran, V., Sabareesh, G.R., Ramachandran, K,I., 2008. Fault diagnosis of roller bearing using kernel based neighborhood score multi-class support vector machine, Expert Systems with Applications 34, 3090-3098. Radhika, R., Sabareesh, G.R., Jagadanand, G., Sugumaran, V., 2010. Precise wavelet for current signature in 3φ IM, Expert Systems with Applications 37, 450-455. Sugumaran, V., Ramachandran, K.I., 2011. Effect of number of features on classification of roller bearing faults using SVM and PSVM, Expert Systems with Applications, 38, 4088-4096.