Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm

Yang, Dalian; Liu, Yilun; Li, Songbai; Li, Xuejun; Ma, Liyong

doi:10.1016/j.mechmachtheory.2015.03.013

Cited by 125 publications

(72 citation statements)

References 16 publications

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“…The fractures, over 31-teeth F114 steel gears, have been carried out by mechanical fatigue tests. It must be noted that fracture tooth represents a challenging mechanical gear fault scenario [30]. Thus, by means of laboratory testing machinery, gears were subjected to fatigue cycles until the elastic module of the material was reached and a fracture appeared.…”

Section: Resultsmentioning

confidence: 99%

Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

Delgado-Prieto

Millán

2017

IEEE Trans. Ind. Electron.

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Section: Resultsmentioning

confidence: 99%

Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

Delgado-Prieto

Millán

2017

IEEE Trans. Ind. Electron.

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“…Wang and Too [223] applied the unsupervised NNs, selforganizing map (SOM), and learning vector quantization in rotating machinery fault diagnosis. Wang et al [225] proposed a method of fault diagnosis for non-stationary fault signals of rotating machineries, which used EEMD and a SOM NN to extract features and classify them, respectively.…”

Section: Ai Approachesmentioning

confidence: 99%

“…Windodo and Yang [206] surveyed the application of SVM in mechanical fault diagnosis including rolling element bearings, induction motors, machine tools, pumps, compressors, valves, turbines, and so on. Yang et al [225] applied artificial bee colony algorithm for SVM parameter optimization of gearbox fault diagnosis, and found that the accuracy of the artificial bee colony algorithm is higher compared with GA and PSO. Widodo et al [226] studied the incipient fault diagnosis of lowspeed bearings using multi-class relevance vector machine and SVM.…”

Section: Ai Approachesmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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“…Este Estudo tem como objetivo a análise de falha de engrenagens bihelicoidais e foi motivado por casos de falha encontrados por um fabricante de redutores de grande porte, aplicados em turbogeradores em usinas de cogeração -6,6% dos redutores fabricados nos anos de 2002 a 2014 apresentaram quebra de dente. Segundo Yang et al [2], 60% das falhas nos sistemas de transmissão devem-se às engrenagens, o que incorre em paradas (downtime) e reposição de componentes danificados, ocasionando prejuízos econômicos e operacionais.…”

Section: Figuraunclassified

Análise De Falha De Engrenagens De Redutores Grande Porte

Ihara¹,

Tanaka²

2017

Anais Do Congresso Anual Da ABM

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ResumoForam encontrados vários casos de falha de engrenagens de redutores de grande porte, aplicados em turbogeradores em usinas de cogeração. Este trabalho tem como objetivo a análise de falha dessas engrenagens bihelicoidais. Engrenagens operam sob condição de carregamento cíclico variável, logo seus principais modos de falha são fadiga de contato e por flexão de dentes. A engrenagem estudada, fabricada em aço 18CrNiMo7-6, foi utlizada em campo e apresentou fratura em dois dentes. Realizou-se uma análise de espectrofotometria para confirmação da composição química do material base. Uma análise microestrutural foi realizada utilizando microscopia óptica, revelando que a microestrutura corresponde a martensita com austenita retida. Foi feito um perfil de microdureza no plano transversal do dente, que indicou uma camada cementada efetiva de 2.150 μm e dureza de 715 HV0.1 na superficie. Foi feita uma fractografia macroscópica para determinação das caracteristicas da fratura, o que permitiu identificar marcas de praia e deltas de rio. Aliado a isso, foram feitas imagens por microscopia eletrônica de varredura, determinando o local do início da trinca. A partir dessas evidências, pôde-se concluir que o mecanismo de falha predominante foi fadiga por flexão de dente. Palavras-chave: Análise de falha; Engrenagens; Fractografia; Fadiga. FAILURE ANALYSIS OF GEARS OF LARGE TRANSMISSION SYSTEMS AbstractSeveral cases of gear failure occurred in large transmission systems, applied to turbogenerators in cogeneration power plants. This work aims to do the failure analysis of the material of these double helical gears. Gears operate under variable cyclic stress, hence the main modes of failure are contact and bending fatigue. The studied gear, manufactured in 18CrNiMo7-6 steel, worked on the field and presented fracture of two teeth. An analysis of spectrophotometry confirmed the chemical composition of the base material. A microstructural analysis was conducted by means of an optical microscope, which revealed that the microstructure corresponds to martensite and retained austenite. A microhardness profile was completed, which denoted an effective carburised layer of 2,150 μm and hardness of 715 HV0.1 on the surface. A macroscopical fractography was done, in order to determine features of the fracture, which allowed for identification of beach marks and river deltas. In addition, imaging by scanning electron microscopy determined the crack iniation site. Based on this evidence, it could be concluded that the predominant failure mechanism was tooth bending fatigue.

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Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm

Cited by 125 publications

References 16 publications

Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

Chromatic Monitoring of Gear Mechanical Degradation Based on Acoustic Emission

Basic research on machinery fault diagnostics: Past, present, and future trends

Análise De Falha De Engrenagens De Redutores Grande Porte

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