Journal-bearing fault detection based on vibration analysis using feature selection and classification techniques

A B ST R A C T Vibration technique in a machine condition monitoring provides useful reliable information, bringing significant cost benefits to industry. By comparing the signals of a machine running in normal and faulty conditions, detection of faults like journal-bearing defects is possible. This paper presents an appropriate procedure for the fault detection of main engine journalbearing based on vibration analysis. The frequency-domain vibration signals of an internal combustion engine (IC engine) with normal and defective main journal-bearings were obtained. The signal processing technique plays one of the important roles for recognizing the journal-bearing fault in the proposed system. In the present research, the data mining method based on feature extraction and selection is proposed. The database is established by the feature vectors of frequency domain signals which are used as input pattern in the training and identification process. The SVM and KNN is proposed to identify and classify the journal-bearing fault conditions in the condition monitoring system. The experimental results verified that the proposed diagnostic procedure has more possibilities and abilities in the fault diagnosis of the main journal-bearing of IC engine.

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