A fuzzy transition based approach for fault severity prediction in helical gearboxes

被引：18

作者：

Cerrada, Mariela ^{[1
,2
]}

Li, Chuan ^{[3
]}

Sanchez, Rene-Vinicio ^{[2
]}

Pacheco, Fannia ^{[2
]}

Cabrera, Diego ^{[2
]}

de Oliveira, Jose Valente ^{[4
]}

机构：

[1] Univ Los Andes, CEMISID, Merida, Venezuela

[2] Univ Politecn Salesiana, GIDTEC Mech Engn Dept, Cuenca, Ecuador

[3] Chongqing Technol & Business Univ, Natl Res Base Intelligent Mfg Serv, Chongqing 400067, Peoples R China

[4] Univ Algrave, Faro, Portugal

来源：

FUZZY SETS AND SYSTEMS | 2018年 / 337卷

关键词：

Fuzzy transition probability; Fuzzy prediction; Fault severity prediction; Fault severity classification; Fault detection and diagnosis; MONITORING PROGRESSIVE FAULTS; ADAPTIVE NEURO-FUZZY; ROTATING MACHINERY; DIAGNOSIS METHOD; INTELLIGENT DIAGNOSIS; FEATURE-SELECTION; EXPERT-SYSTEM; BEARING; PROGNOSTICS; IDENTIFICATION;

D O I：

10.1016/j.fss.2016.12.017

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Rotating machinery is an important device supporting manufacturing processes, and a wide research works are devoted to detecting and diagnosing faults in such machinery. Recently, prognosis and health management in rotating machinery have received high attention as a research area, and some advances in this field are focused on fault severity assessment and its prediction. This paper applies a fuzzy transition based model for predicting fault severity conditions in helical gears. The approach combines Mamdani models and hierarchical clustering to estimate the membership degrees to fault severity levels of samples extracted from historical vibration signals. These membership degrees are used to estimate the weighted fuzzy transitions for modelling the evolution along the fault severity states over time, according to certain degradation path. The obtained fuzzy model is able of predicting the one step-ahead membership degrees to the severity levels of the failure mode under study, by using the current and the previous membership degrees to the severity levels of two available successive input samples. This fuzzy predictive model was validated by using real data obtained from a test bed with different damages of tooth breaking in the helical gears. Results show adequate predictions for two scenarios of fault degradation paths. (c) 2016 Elsevier B.V. All rights reserved.

引用

页码：52 / 73

页数：22

共 83 条

[1]

Amar M, 2013, 2013 IEEE EIGHTH INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING, P420, DOI 10.1109/ISSNIP.2013.6529827

[2]

[Anonymous], 2016, ISA T

[3]

[Anonymous], INDIAN J SCI TECHNOL

[4]

[Anonymous], INT J DATABASE THEOR

[5]

[Anonymous], INT J HYBRID INF TEC

[6]

[Anonymous], RES J APPL SCI ENG T

[7]

[Anonymous], ADV APPL MECH RES C

[8]

[Anonymous], 2011, IND AEROSPACE AUTOMO

[9]

Beer Michael, 2010, Proceedings of the 2010 IEEE International Conference on Granular Computing (GrC-2010), P5, DOI 10.1109/GrC.2010.78

[10] Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition [J].

Cabrera D. ;

Sancho F. ;

Sánchez R.-V. ;

Zurita G. ;

Cerrada M. ;

Li C. ;

Vásquez R.E. .

Frontiers of Mechanical Engineering, 2015, 10 (3) :277-286

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