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A methodology for identifying information rich frequency bands for diagnostics of mechanical components-of-interest under time-varying operating conditions
Mechanical Systems and Signal Processing ( IF 8.4 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.ymssp.2020.106739 Stephan Schmidt , Alexandre Mauricio , P. Stephan Heyns , Konstantinos C. Gryllias
Mechanical Systems and Signal Processing ( IF 8.4 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.ymssp.2020.106739 Stephan Schmidt , Alexandre Mauricio , P. Stephan Heyns , Konstantinos C. Gryllias
Abstract Performing condition monitoring on rotating machines such as wind turbines, which operate inherently under time-varying operating conditions, remains a challenge. The signal components generated by incipient damage are masked by other signal components that are not of interest and high noise levels. In this work, a new method, referred to as the IFBI α gram, is proposed that is capable of identifying frequency bands that are rich with diagnostic information related to specific cyclic components. This allows the optimal frequency band to be determined for diagnosing the component-of-interest. It is shown on numerical and experimental gearbox data that this method is not only capable of detecting incipient damage, but is also robust to time-varying operating conditions. Therefore, it can be used to independently determine the condition of different mechanical components and it is robust to spurious transients.
中文翻译:
一种识别信息丰富的频带的方法,用于在时变操作条件下诊断感兴趣的机械部件
摘要 对风力涡轮机等旋转机器进行状态监测,这些机器在时变运行条件下固有地运行,仍然是一个挑战。由早期损坏产生的信号分量被其他不感兴趣的信号分量和高噪声电平所掩盖。在这项工作中,提出了一种称为 IFBI α gram 的新方法,该方法能够识别富含与特定循环分量相关的诊断信息的频带。这允许确定用于诊断感兴趣组件的最佳频带。数值和实验齿轮箱数据表明,该方法不仅能够检测初期损坏,而且对时变操作条件也具有鲁棒性。所以,
更新日期:2020-08-01
中文翻译:
一种识别信息丰富的频带的方法,用于在时变操作条件下诊断感兴趣的机械部件
摘要 对风力涡轮机等旋转机器进行状态监测,这些机器在时变运行条件下固有地运行,仍然是一个挑战。由早期损坏产生的信号分量被其他不感兴趣的信号分量和高噪声电平所掩盖。在这项工作中,提出了一种称为 IFBI α gram 的新方法,该方法能够识别富含与特定循环分量相关的诊断信息的频带。这允许确定用于诊断感兴趣组件的最佳频带。数值和实验齿轮箱数据表明,该方法不仅能够检测初期损坏,而且对时变操作条件也具有鲁棒性。所以,