Knowledge discovery for gearbox fault diagnosis using flow graph

2016 ◽  
Author(s):  
Jun Yu ◽  
Wentao Huang ◽  
Xuezeng Zhao
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Discovery ◽  
Flow Graph

scholarly journals Methodology to knowledge discovery for fault diagnosis of hybrid dynamical systems: demonstration on two tanks system

Diagnostyka ◽  
2020 ◽  
Vol 21 (4) ◽  
pp. 115-123
Author(s):  
Mohammed Said Achbi ◽  
Lotfi Mhamdi ◽  
Sihem Kechida ◽  
Hedi Dhouibi
Keyword(s):  
Fault Diagnosis ◽  
Dynamical Systems ◽  
Knowledge Discovery ◽  
Hybrid Dynamical Systems

Knowledge Discovery in Incomplete Medical Data Using Flow-Graph Networks

2015 ◽  
Vol 713-715 ◽  
pp. 2133-2138
Author(s):  
Yan Wang ◽  
Lu Bin Hang ◽  
Feng Liu ◽  
Bin Jiang
Keyword(s):  
Knowledge Discovery ◽  
Incomplete Data ◽  
Medical Data ◽  
Data Flow Graph ◽  
Certainty Factor ◽  
Flow Graph ◽  
Data Problem ◽  
Flow Graphs ◽  
Discovery Algorithms ◽  
Computing Method

Clinical data are inevitably incomplete, and most knowledge discovery algorithms lack the capability to contend with missing data. Flow-graph confers some distinct advantages in data mining and knowledge discovery. However, flow-graph methodology is not able to comprehensively solve the incomplete data problem. This paper proposes a flow-graph network approach for extracting knowledge from incomplete medical data. The concept of incomplete-medical-diagnosis-flow-graph (IMDFG) was defined. To evaluate the diagnosis rules within theIMDFG, the computing method for the certainty factor and coverage factor are presented. Moreover, the application of flow-graph network can be useful for extracting comprehensibility knowledge from the incomplete medical data. In an illustrative medical example, the clinical diagnosis rules are induced and interpreted in accordance to the generated flow graphs.

A data mining approach for lubricant-based fault diagnosis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
James Wakiru ◽  
Liliane Pintelon ◽  
Peter Muchiri ◽  
Peter Chemweno
Keyword(s):  
Decision Support ◽  
Fault Diagnosis ◽  
Knowledge Discovery ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Gradient Boosting ◽  
Data Set ◽  
Content Type ◽  
Embedded Knowledge ◽  
Extreme Gradient Boosting

PurposeThe purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set.Design/methodology/approachThe DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models.FindingsThe results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs.Practical implicationsThe proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.Originality/valueAdvances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS.

Spur Bevel Gearbox Fault Diagnosis Based on Wavelet Packet Transform for Feature Extraction and Flow Graph Data Mining

2013 ◽  
Vol 753-755 ◽  
pp. 2297-2302
Author(s):  
Wen Tao Huang ◽  
Pei Lu Niu ◽  
Yin Feng Liu ◽  
Wei Jie Wang
Keyword(s):  
Data Mining ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Wavelet Packet Transform ◽  
Mining Method ◽  
Flow Graph ◽  
Data Mining Method ◽  
Attribute Information

Gearbox vibration signal contains a wealth of the gear status information, used wavelet packet transform (WPT) refinement of the partial lock ability to extract the fault signs attribute information in the vibration signal. Extracted signs attribute information as the input of the flow graph (FG), generated decision rules to achieve the purpose of fault diagnosis. FG was a knowledge representation and data mining method to mine the intrinsic link between the data and improve the clarity of the potential knowledge. The results confirmed that used of WPT feature extraction and FG data mining method can accurate detection the gear fault.

Combined flow graphs and normal naive Bayesian classifier for fault diagnosis of gear box

2015 ◽  
Vol 230 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Jun Yu ◽  
Wentao Huang ◽  
Xuezeng Zhao
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Classifier ◽  
Minimal Flow ◽  
Flow Graph ◽  
Naïve Bayesian Classifier ◽  
Naive Bayesian ◽  
Naive Bayesian Classifier ◽  
Naïve Bayesian ◽  
Gear Box ◽  
Flow Graphs

In order to improve the intuition, efficiency, and accuracy of fault diagnosis of gear box, a novel fault diagnosis method based on flow graphs and normal naive Bayesian classifier is proposed in this paper. In the proposed method, flow graphs are utilized to represent the relationship between fault symptoms and gear conditions. The algorithm of layer reduction is employed to eliminate the redundant and irrelevant attribute layers to obtain the minimal flow graph for reducing the number of input nodes in normal naive Bayesian classifier. The normal naive Bayesian classifier is constructed according to the minimal flow graph to obtain classification results. To verify the proposed method, an experiment is carried out to apply this method to a gear box rig. The experiment results demonstrate that the proposed method combining the advantages of flow graphs and normal naive Bayesian classifier provides a new way to design high-performance models for fault diagnosis of gear box.

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