Condition Monitoring of an Industrial Oil Pump Using a Learning Based Technique

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 appropriate distance criterion function. Moreover, our optimization algorithm chooses the most appropriate feature submatrix to improve the final accuracy in an iterative method. As a case study, the proposed algorithms are applied to experimental data gathered from an industrial heavy-duty oil pump installed in Arak Oil Refinery Company. The experimental results are very promising.