Abstract
Individual condition monitoring (CM) strategies are capable to diagnose 30–40% of the defects, when they are performed individually. However, combining two or more individual CM strategies can provide more reliable information which will enhance the ability of fault detection. In this investigation, two intrusive CM strategies (vibration and lubrication oil analysis) and one non-intrusive CM strategy (acoustic signal analysis) are combined to form an integrated CM scheme. Experiments are performed on a miniature wind turbine gearbox bench top and the raw data is acquired and the defect sensitive features are extracted using discrete wavelet transform. Feature level fusion is accomplished to achieve integrated feature data set and the selection of optimal subset of significant features is done by various supervised featured selection methods. Finally, the obtained optimal feature subset is classified using SVM algorithm in order to diagnose the local defects of bearings as well as gears present in different stages of the wind turbine gearbox.
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References
Stetco A, Dinmohammadi F, Zhao X, Robu V, Flynn D, Barnes M, Nenadic G (2018) Machine learning methods for wind turbine condition monitoring: a review. Renew Energy 133:620–635
Vamsi I, Sabareesh GR, Penumakala PK (2019) Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading. Mech Syst Signal Process 124:1–20
Amarnath M, Sugumaran V, Kumar H (2013) Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement 46(3):1250–1256
Radhika S, Sabareesh GR, Jagadanand G, Sugumaran V (2010) Precise wavelet for current signature in 3ϕ IM. Expert Syst Appl 37(1):450–455
Inturi V, Sabareesh GR, Supradeepan K, Penumakala PK (2019) Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox. J Vib Control 25(12):1852–1865
Nembhard AD, Sinha JK, Pinkerton AJ, Elbhbah K (2014) Combined vibration and thermal analysis for the condition monitoring of rotating machinery. Struct Health Monit 13(3):281–295
Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–79
Raschka S (2018) MLxtend: providing machine learning and data science utilities and extensions to Python’s scientific computing stack. J Open Source Softw 3(24):638
Konar A (2000) Artificial intelligence and soft computing: behavioral and cognitive modeling of the human brain. CRC Press, Florida
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Inturi, V., Ritik Sachin, P., Sabareesh, G.R. (2020). Supervised Feature Selection Methods for Fault Diagnostics at Different Speed Stages of a Wind Turbine Gearbox. In: Goel, N., Hasan, S., Kalaichelvi, V. (eds) Modelling, Simulation and Intelligent Computing. MoSICom 2020. Lecture Notes in Electrical Engineering, vol 659. Springer, Singapore. https://doi.org/10.1007/978-981-15-4775-1_51
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DOI: https://doi.org/10.1007/978-981-15-4775-1_51
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