Évènements

25 Mayo
25/05/2022 14:00

Sciences & Société

Soutenance de thèse : Yaqiang JIN

Integrated auto-diagnosis based on stochastic model for rolling element bearings

Doctorant : Yaqiang JIN

Laboratoire INSA : LVA

Ecole doctorale : ED162 : Mécanique, Energétique, Génie Civil, Acoustique de Lyon

Today, the most fundamental issue of condition monitoring in most industrial plants is the fault diagnostics and prognostics. One of the most effective approaches to investigate this issue is condition monitoring based on vibration signal analysis. With the development of industry, multi-threaded maintenance and multi-channel acquisition are becoming more widespread in the current, which put forward higher requirements for maintenance. Based on this observation, it is proposed in this thesis one automated diagnosis framework for the rolling element bearing that integrates the successive steps of fault detection, fault type identification, fault signal reconstruction and fault size characterization. The advantage is that the complete diagnosis process is completed at once, while involving only one key hyperparameter, which improves the degree of automation of current Condition Based Maintenance (CBM) and liberating human participation.
In the presence of incipient fault, vibrations of rolling element bearings show symptomatic signatures in the form of repetitive impulses. This can be seen as a non-stationary signal whose statistical properties switch between two states. The proposed maintenance strategy models such characteristics with an explicit-duration hidden Markov model (EDHMM) and uses the estimated model parameters to perform integrated diagnosis without requiring the user's expertise. The detection of a fault is first achieved by means of a likelihood ratio test built on the EDHMM parameters. One statistical counting approach and posterior probability spectrum are then used for identifying the fault type automatically. In order to obtain the fault signal in some case, one Bayesian filter based on the EDHMM parameters is constructed. Finally, the fault size is estimated from the duration times returned by EDHMM.
Subsequently, the capability of the integrated auto-diagnosis framework is illustrated on different experimental datasets. The first validation is forced on the vibration data for specific conditions. The results indicate that the robust and accurate maintenance of the rolling element bearing. In addition, the result of accelerated degradation data also shows the effectiveness of the method, especially the ability of detecting failure occurrence and tracking quantitatively fault development. This technique has potential for using in the machine CBM.

 

Información adicional

  • Amphithéâtre Clémence Augustine Royer (321-01-04) (Bâtiment Jacqueline FERRAND) (Villeurbanne)

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