ArticleEntropy (Basel, Switzerland)2018
A Feature Extraction Method Using Improved Multi-Scale Entropy for Rolling Bearing Fault Diagnosis.
Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Obtaining Information about Operation of Centrifugal Compressor from Pressure by Combining EEMD and IMFE.Entropy (Basel, Switzerland) · 2020Article
- Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals.Entropy (Basel, Switzerland) · 2019Article
- Attention to the Variation of Probabilistic Events: Information Processing with Message Importance Measure.Entropy (Basel, Switzerland) · 2019Article
- Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network.Entropy (Basel, Switzerland) · 2019Article
- Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2018Article
- Recognizing Information Feature Variation: Message Importance Transfer Measure and Its Applications in Big Data.Entropy (Basel, Switzerland) · 2018Article
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Authors and funding
6 authors.
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Abstract
A feature extraction method named improved multi-scale entropy (IMSE) is proposed for rolling bearing fault diagnosis. This method could overcome information leakage in calculating the similarity of machinery systems, which is based on Pythagorean Theorem and similarity criterion. Features extracted from bearings under different conditions using IMSE are identified by the support vector machine (SVM) classifier. Experimental results show that the proposed method can extract the status information of the bearing. Compared with the multi-scale entropy (MSE) and sample entropy (SE) methods, the identification accuracy of the features extracted by IMSE is improved as well.
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