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Contributions to early-chatter detection and period-N bifurcations identification based on cumulative diagnosis approach

Abstract : Cumulative diagnosis of dynamic systems requires the detection, identification, and characterization of incipient degradations. Its application to high-speed machining, for instance, could rely on period-N bifurcations phenomena analysis to detect and identify early-chatters and improve the quality of milling products and processes. Up to now, many efficient methods were proposed to detect early-chatter and identify period-N bifurcations. But these methods are struggling to implement these tasks reliably and accurately due to the complex nonlinear characteristics of their dynamic behaviors, the noise, and the variation of their operating conditions. The present thesis aims to develop and implement methods of early-chatter detection and period-N bifurcations identification within a real-time cumulative diagnosis approach. Aimed at early-chatter detection, we proposed three detection methods and one identification method for the cumulative diagnosis. The first method can be used to detect early-chatters remotely. The second one detects early-chatter quickly under specific operating and measuring conditions. However, in practice, the operating and measuring conditions are complex and variable. To adapt to different operating and measuring conditions, we proposed a third method, and the latter detects early-chatter reliably. It is also noted that in milling processes, the early-chatter can give rise to a bifurcation of period-N or Hopf type. The machining quality under the bifurcation process of the period-N type is less critical than that under the Hopf bifurcation type. To improve machining productivity and ensure the required machining quality, we can mill the workpiece under the condition of period-N bifurcations. Thus, it is compulsory to identify the early period-N bifurcations for improving machining productivity. For that purpose, we developed a method for identifying the type and size of the period-N bifurcations. We also proved the effectiveness of the proposed methods, using two benchmark milling process models. Besides, the proposed methods can be used for fault diagnosis of other dynamic systems, such as the pulse energy conversion systems or bearing or gearing systems.
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Submitted on : Wednesday, June 30, 2021 - 1:14:54 PM
Last modification on : Thursday, July 1, 2021 - 3:17:46 AM
Long-term archiving on: : Friday, October 1, 2021 - 6:31:01 PM


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  • HAL Id : tel-03274706, version 1


Yanqing Zhao. Contributions to early-chatter detection and period-N bifurcations identification based on cumulative diagnosis approach. Automatic. Université de Lorraine, 2020. English. ⟨NNT : 2020LORR0250⟩. ⟨tel-03274706⟩



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