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Degradation State Identification for Ceramic in Ultrasonic Motor Based on Morphological Boundary Span Analysis

机译:基于形态边界跨度分析的超声波电动机陶瓷降解状态鉴定

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摘要

Piezoelectric ceramic cracking (PCC) is the main reason leading to failure of ultrasonic motors. To solve the problem that the fault information is too weak to reflect the cracking condition especially in the early degradation stage, a degradation state identification method based on morphological boundary span coverage statistics was proposed in this paper. Firstly, the average morphological cover area of standard data was adopted as the degradation feature for PCC. Then standard degradation state rectangles (SDSRs) were constructed based on the degradation feature. The height of SDSR was optimized to improve the classification accuracy via training data with the help of genetic algorithm. Lastly, the coverage statistics obtained by the relationship between test data's morphological boundary span signal and the constructed SDSR can be taken as a fair indicator for the actual degradation state. The experimental results show that this method is feasible and effective, and could achieve a satisfying performance to identify the different degradation states of PCC.
机译:压电陶瓷开裂(PCC)是导致超声波电机故障的主要原因。为了解决故障信息太弱而无法反映裂缝条件,特别是在早期降解阶段,本文提出了一种基于形态边界覆盖统计的降解状态识别方法。首先,采用标准数据的平均形态覆盖面积作为PCC的降解特征。然后,基于劣化特征构建标准劣化状态矩形(SDSR)。通过遗传算法的帮助,优化了SDSR的高度,以通过培训数据来提高分类准确性。最后,通过测试数据的形态边界信号与构造的SDSR之间的关系获得的覆盖统计数据可以作为实际劣化状态的公平指示。实验结果表明,该方法是可行且有效的,可以实现识别PCC的不同降解状态的满意性能。

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