9 research outputs found
On-line Time Domain Vibration and Current Signals Based Multi-fault Diagnosis of Centrifugal Pumps Using Support Vector Machines
No full textData-Driven Fault Classification Using Support Vector Machines
No full textDetecting faulty condition of rolling-element bearings is significant in improving system reliability and preventing machine failure in industrial operations. In this paper, a machine learning pipeline is developed using filtered data through time domain features to train support vector machines with radial basis function, polynomial and linear kernels for multi-level fault diagnosis and classification. Overall accuracy rate and F-score values were used as figures of merit to evaluate and validate the performance of the machine learning model. SVM classifier showed significantly high overall accuracy rate of 91% to 99% and F-score of 0.81 to 0.99 with time domain statistical features due to the capability of this method in elimination of irrelevant features as well as reducing the dimensionality of the data. In addition, the high accuracy rate of SVMs in class-specific detection of localized bearing faults show the significant potential of data-driven classification modeling for fault detection in industrial and manufacturing operations
Blind Application of Developed Smart Vibration-Based Machine Learning (SVML) Model for Machine Faults Diagnosis to Different Machine Conditions
No full textAn Experimental Approach for Detection and Quantification of Short-Circuit on a Doubly Fed Induction Machine (DFIM) Windings
No full textAutomation of multi-fault diagnosing of centrifugal pumps using multi-class support vector machine with vibration and motor current signals in frequency domain
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