Rolling bearing fault diagnosis based on EEMD sample entropy and PNN
TLDR
A fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) sample entropy and probabilistic neural network (PNN) is proposed for non-steady and non-linear signals, which proves the effectiveness of the proposed method.Abstract:
A fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) sample entropy and probabilistic neural network (PNN) is proposed for non-steady and non-linear signals. First, the rolling bearing signals are decomposed into intrinsic mode function (IMF) using EEMD. Then, the kurtosis of each component is calculated. Five components with large kurtosis are selected and the sample entropy is extracted to form the feature vectors. Finally, the feature vectors are input to the PNN for fault diagnosis. The method is used to classify the type of the rolling bearing fault. The results show that the accuracy of fault diagnosis of the proposed method is 100%, which proves the effectiveness of the proposed method.read more
Citations
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Bearing Fault Diagnosis Using a Particle Swarm Optimization-Least Squares Wavelet Support Vector Machine Classifier.
TL;DR: This paper develops a novel particle swarm optimization-least squares wavelet support vector machine (PSO-LSWSVM) classifier, which is designed based on a combination between a PSO, a least squares procedure, and a new wavelet kernel function-based support Vector machine (SVM), for bearing fault diagnosis.
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Analog circuit fault diagnosis based on density peaks clustering and dynamic weight probabilistic neural network
Junyou Shi,Yi Deng,Zili Wang +2 more
TL;DR: A novel analog circuit fault diagnosis method based on density peaks clustering and a dynamic weight PNN that can achieve high classification accuracy with only a few pattern neurons is proposed.
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Application of a whale optimized variational mode decomposition method based on envelope sample entropy in the fault diagnosis of rotating machinery
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A Data-Driven Fault Diagnosis Method Using Modified Health Index and Deep Neural Networks of a Rolling Bearing
TL;DR: A data-driven fault diagnosis model based on the adjustment Mahalanobis-Taguchi system (AMTS) that can analyze and identify the characteristics of vibration signals by using degradation monitoring as the classifier to capture and recognize the faults of product more accurately is proposed.
References
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Using vibration monitoring for local fault detection on gears operating under fluctuating load conditions
TL;DR: In this article, a vibration waveform normalisation approach is presented, which enables the use of the pseudo-Wigner-Ville distribution to indicate deteriorating fault conditions under fluctuating load conditions.
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Design and Analysis of Internal Compound Cycloid Gear Transmission with High Contact Ratio
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Gas and Liquid Flow Rate Measurement in Wet Gas Based on Differential Pressure Signals
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