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Detection and localization of fatigue-induced transverse crack in a rotor shaft using principal component analysis

机译:使用主成分分析检测和定位转子轴中的横向横裂缝

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摘要

Rotor shafts subjected to severe operating stresses are prone to develop transverse fatigue cracks at the localized stress raisers. Therefore, the ability to identify and locate the incipient fatigue crack is imperative in order to avoid catastrophic failure. The literature on rotor crack detection discussed the importance of monitoring the steady-state 1X, 2X and 3X harmonic response components of rotors. However, the other rotor faults such as misalignment and unbalance, exhibit similar symptoms. Thus, the main aim is to develop new independent fault-related features which measure the driving principle governing the behaviour of various rotor faults. In this article, the application of principal component analysis–based statistical pattern analysis, as a tool for early detection and localization of fatigue-induced transverse crack in a rotor shaft is investigated. To perform this study, accelerated fatigue experiments are conducted on a customized setup. This developed test rig is novel and unique by itself that facilitates generating a fatigue crack in a shaft, under conditions that mimic a real in-service loading environment of industrial rotors. Unlike conventional methods, noise in the acquired vibration and strain data is denoised via classical principal component analysis method. Time- and frequency-domain statistical features extracted from different vibration and strain sensor signals are used for this study. Damage indices such as Hotelling’s T ~(2)-statistic and Q -index are used to detect the presence of the crack. It is observed that irrespective of the sensor location, damage index such as Q-statistic of all the sensors is very effective to detect the presence and time of incipient crack. Partial decomposition contributions method is found to be very effective in identifying the location of the crack. This article provides the most significant vibration-based statistical features, which are sensitive to shaft transverse cracks, for different sensor types and their mounting location. Finally, a new fused health indicator which is highly sensitive to the presence of rotor shaft crack is defined and is found successful when applied to a new experimental data.
机译:经受严重操作应力的转子轴容易发生在局部应力升降机处产生横向疲劳裂缝。因此,识别和定位初期疲劳裂纹的能力是必要的,以避免灾难性的失败。转子裂纹检测的文献讨论了监测转子的稳态1x,2x和3x谐波响应分量的重要性。然而,其他转子断层如错位和不平衡,表现出类似的症状。因此,主要目的是开发新的独立故障相关的特征,该特征测量各种转子故障行为的驱动原理。在本文中,研究了基于主成分分析的统计模式分析的应用,作为转子轴中的疲劳诱导的横裂的早期检测和定位的工具。为了进行这项研究,在定制的设置上进行加速疲劳实验。该开发的试验台本身是新颖的,其本身是独特的,便于在模拟工业转子的真正役装载环境的条件下产生轴中的疲劳裂缝。与传统方法不同,通过经典主成分分析方法对所获取的振动和应变数据进行噪声。从不同振动和应变传感器信号提取的时间和频域统计特征用于本研究。损坏诸如Hotelling的T〜(2) - Q-(2) - Q -Index的损坏指数用于检测裂缝的存在。观察到,无论传感器位置如何,诸如所有传感器的Q统计的损伤指数非常有效地检测初始裂纹的存在和时间。发现部分分解贡献方法在识别裂缝的位置非常有效。本文提供了最重要的基于振动的统计特征,对轴横向裂缝敏感,用于不同的传感器类型及其安装位置。最后,在应用于新的实验数据时,定义了对转子轴裂纹的存在非常敏感的新融合健康指标。

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