Amin Ranjbar1, Amir Abolzafl Suratgar1,*, Saeed Shiry Ghidary2, Jafar Milimonfared3
Sound & Vibration, Vol.54, No.4, pp. 257-267, 2020, DOI:10.32604/sv.2020.05055
Abstract This paper proposes an efficient learning based approach to detect the faults of an industrial oil pump. The proposed method uses the wavelet transform and genetic algorithm (GA) ensemble for an optimal feature extraction procedure. Optimal features, which are dominated through this method, can remarkably represent the mechanical faults in the damaged machine. For the aim of condition monitoring, we considered five common types of malfunctions such as casing distortion, cavitation, looseness, misalignment, and unbalanced mass that occur during the machine operation. The proposed technique can determine optimal wavelet parameters and suitable statistical functions to exploit excellent features via an… More >
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