Intelligent Identification of Bearing Faults Using Time Domain Features

Wu, Chenxi; Ning, Liwei; Jiang, Rong; Liu, Junan

doi:10.1109/icdma.2013.169

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…Patel and Giri [13] extracted 15 statistical features and classified them using a neural network (NN) and random forest (RF) classifier. Also, Chenxi et al [14] used five-time domain features to classify faults using vibration data. The researchers attempted to identify the bearing fault classification using the Fast Fourier transform (FFT) and power spectral density.…”

Section: Introductionmentioning

confidence: 99%

Pattern classification of bearing faults in PMSM based on time domain feature ensembles

2024

Eng. Res. Express

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This paper aims to identify an effective pattern classification methodthat can be employed using vibration and current data to identify bearing conditions.The authors attempted non-conventional time-domain features to detect the bearingconditions in permanent magnet synchronous motors (PMSM). This study uses twocase studies with eight datasets from Paderborn University to identify the bearingconditions of 3 and 12 classes. Support vector machine, k-nearest neighbor, randomforest, decision tree, and naive Bayes classifiers are attempted with 10% holdoutvalidation for 4 data sets with 31 feature ensembles. Also, this paper investigates theHenry Gas Solubility Optimization (HGSO) feature selection approach for identifyingthe most discriminant features. The effectiveness of these discriminant features isverified with three bearing conditions diagnosis. Results have shown, that four featureensembles with 2 to 10 features outperformed support vector machine, k-nearestneighbor, and random forest classifiers. In contrast to previous relevant studies, theproposed features are useful in identifying PMSM-bearing conditions with excellentaccuracy in vibration and combined current signals under a wide range of operatingconditions.

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Section: Introductionmentioning

confidence: 99%