Reconstruction of Vital Blade Signal from Unsteady Casing Vibration

Some important information pertaining to blade fault is thought to be concealed in highly unsteady casing vibration. This paper explores suitable methods to best reconstruct blade related signals from raw casing vibration, which could be used for diagnosis of blade fault. The feasibility of translation invariant wavelet transform and cycle spinning (TIWT-CS) technique in reconstruction of these signals is investigated in this paper. Subsequently, a new parameter for blade fault diagnosis, namely, the energy profile of blade signal (EPBS), is formulated. Experimental results show that TIWT-CS method effectively retained blade related signals, while other unwanted signals such as system noises and aerodynamic induced vibration are reasonably suppressed. EPBS provides an indication of the condition of blade faults in rotor system, whereby the exact position and the quantity of faulty blades, as well as the root cause of blade fault, can be identified. In comparison, the energy profile plots using unfiltered casing vibration were found to be highly unstable and therefore provides inconsistent results for diagnosis of blade fault. 1. Introduction Blade fault is one of the most destructive and elusive problems in power generation and aerospace industries. The most common types of blade fault include blade rubbing, low and high cycle fatigue failures, blade creep, fouled blade, loose blade, and blade induced foreign object damage (FOD). Undetected blade fault could further deteriorate to trigger some serious consequences such as in the event of FOD that could potentially undermine the functionality and total integrity of the machine. Traditionally, detection of blade fault is often conducted via spectrum analysis of bearing vibration. This method monitors changes in the vibration spectrum and in particular the amplitude of blade pass frequency (BPF) and its sidebands components. Any abnormal changes in the amplitudes of these frequencies together with the presence of some peculiar vibration peaks in the vibration spectrum could indicate the occurrence of blade fault. This method has been studied by Kubiak et al. [1] and Simmons ([2, 3] and Parge et al. [4] and Parge [5]), amongst others. In more recent years, the application of advanced signal analysis techniques such as wavelet analysis and artificial intelligence methods to detect blade fault has been reported. Angelakis et al. [6] applied neural networks technique to classify healthy and faulty blade condition based on experimental study. Peng et al. [7] used the reassigned wavelet scalograms to improve the