Diagnosis of Bearing Faults of Induction Motors by Spectral Analysis of Stator Currents

Safin, Nail; Prakht, Vladimir; Dmitrievskii, Vladimir; Dmitrievskii, A. A.

doi:10.4028/www.scientific.net/amr.1070-1072.1187

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…In the past decades, a variety of methods have been developed in bearing fault diagnosis, such as vibration analysis [ 1 ], acoustic analysis [ 2 ], noise analysis [ 3 ], thermal imaging analysis [ 4 ] and so on, among which the vibration analysis has proven to be the most efficient [ 5 ]. Many vibration signal processing tools have been used in signal preprocessing such as Fourier spectral analysis [ 6 ], wavelet analysis [ 7 ], empirical mode decomposition [ 8 ], and multi-wavelet transformation [ 9 ]. These vibration analysis methods have achieved good performance from non-adaptive analysis to adaptive analysis and from qualitative analysis to quantitative analysis [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

Liu

Tang

et al. 2017

Sensors

180

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Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

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