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

Abstract

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 T2-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.

An Improved Crack Breathing Model and Its Application in Crack Identification for Rotors

“…In [24], a modern model-based technique with the proportional-integral observer was put forward for crack identification in an operating rotor, which was compared with a signal-based technique with wavelets and the support vector machine. In [25], principal component analysis was utilized to construct a damage index for crack detection and localization. In [26], orbit morphological characteristics were extracted for crack detection.…”

Effect of Thermal Response and Vibration on the Multicracked Rotor Immersed in a Fluid Medium Using Finite Element Method

“…Babu Rao and Mallikarjuna [27] proposed the ANN technique for damage detection in rotor shaft systems using the discrete wavelet transform technique. The principal component analysis was employed to calculate the damage index for recognition of the position of the crack [28]. Xiang et al [29] have proposed the orbit morphological characteristics method to obtain the position of the crack.…”