Identification of Critical Design Parameter for Mechanical Products Based on Performance Data

doi:10.3901/JME.2021.03.185

Abstract

Abstract: Identifying the weaknesses or defects of the current design based on the operating data is the main mode of product development. It is also an important way to ensure the stable performance of products. An approach of critical design parameter identification based on performance data is proposed. Firstly, the operating conditions are identified by the algorithm of extreme learning machine. A performance degradation assessment method based on kernel principal component analysis and gaussian mixture model is conducted, which eliminates the influence of operating conditions on performance degradation assessment and obtains key functional modules with severe performance degradation. Secondly, cluster analysis is carried out on operation monitoring data to identify critical monitoring parameters closely related to module performance degradation. Finally, the correlation between "performance monitoring parameter-product performance parameter-design parameter" is established to identify the critical design parameters. The effectiveness of the proposed method is verified by a case study of a large tonnage crawler crane.

Key words: performance monitoring data, performance degradation assessment, critical design parameter identification, Gaussian mixture model, crawler crane

CLC Number:

TH122
TP391

CHU Xuening, CHEN Hansi, MA Hongzhan. Identification of Critical Design Parameter for Mechanical Products Based on Performance Data[J]. Journal of Mechanical Engineering, 2021, 57(3): 185-196.

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