Journal ArticleDOI
Fault severity identification of roller bearings using flow graph and non-naive Bayesian inference:
Jun Yu,Yonggang Xu,Guangbin Yu,Lifei Liu +3 more
- Vol. 233, Iss: 14, pp 5161-5171
TLDR
A node reduction algorithm based on significance degree of condition attribute node is developed to delete redundant or irrelevant condition attribute nodes, which can improve clustering distribution and reduce computational complexity.Abstract:
In order to address the problem that redundant condition attribute nodes and poor reasoning ability of flow graph may lead to high computational burden and low diagnosis accuracy, a fault severity identification method of roller bearings using flow graph and non-naive Bayesian inference is put forward in this paper. First, a normalized flow graph constructed according to fault features of roller bearings extracted from training samples is used to represent and describe the causal relationship among attributes. Then, the significance degree of condition attribute node with respect to the decision attribute node set is defined to quantitatively measure the impact of the node on the decision-making abilities of the flow graph. A node reduction algorithm based on significance degree of condition attribute node is developed to delete redundant or irrelevant condition attribute nodes, which can improve clustering distribution and reduce computational complexity. Finally, non-naive Bayesian inference is utilized...read more
Citations
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Journal ArticleDOI
Fault Diagnosis Method Based on Principal Component Analysis and Broad Learning System
TL;DR: Experimental results show that the PCA method can effectively eliminate the feature correlation and realize the dimension reduction of the feature matrix, the BLS can take on better adaptability, faster computation speed, and higher classification accuracy, and the PABSFD method can efficiently and accurately obtain the fault diagnosis results.
Journal ArticleDOI
Fault Diagnosis in Gas Insulated Switchgear Based on Genetic Algorithm and Density- Based Spatial Clustering of Applications With Noise
TL;DR: The proposed GA-DBSCAN approach can substantially increase the performance of the fault diagnosis method, which indicates that the method promotes development of intelligent detection technology of mechanical state in GIS.
Journal ArticleDOI
Toward cognitive predictive maintenance: A survey of graph-based approaches
TL;DR: Wang et al. as discussed by the authors proposed a graph-based approach (GbA) with cognitive intelligence for predicting the causal relationship of faults and their root causes, which achieved promising performance on PdM perception tasks by revealing the dependency relationship among parts/components of the equipment.
Journal ArticleDOI
Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT
TL;DR: In this article, an improved variational mode decomposition (VMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs).
Journal ArticleDOI
A novel fault diagnosis method based on improved adaptive variational mode decomposition, energy entropy, and probabilistic neural network
TL;DR: A novel bearing fault diagnosis method based on parametric adaptive variational mode decomposition (VMD), energy entropy, and pr... to improve the accuracy of bearing fault recognition.
References
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Journal ArticleDOI
Artificial intelligence for fault diagnosis of rotating machinery: A review
TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.
Journal ArticleDOI
A review on the application of deep learning in system health management
Samir Khan,Takehisa Yairi +1 more
TL;DR: This article presents a systematic review of artificial intelligence based system health management with an emphasis on recent trends of deep learning within the field and demonstrates plausible benefits for fault diagnosis and prognostics.
Journal ArticleDOI
Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
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Journal ArticleDOI
A novel deep autoencoder feature learning method for rotating machinery fault diagnosis
TL;DR: A novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault and the results confirm that the proposed method is more effective and robust than other methods.
Journal ArticleDOI
A review on data-driven fault severity assessment in rolling bearings
Mariela Cerrada,Mariela Cerrada,René-Vinicio Sánchez,Chuan Li,Fannia Pacheco,Diego Cabrera,José Valente de Oliveira,Rafael E. Vásquez +7 more
TL;DR: In this article, a review of fault severity assessment of rolling bearing components is presented, focusing on data-driven approaches such as signal processing for extracting proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions.
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