Sparse reconstruction of blade tip-timing signals for multi-mode blade vibration monitoring. (15th December 2016)

• Record Type: Journal Article
• Authors: Lin, Jun; Hu, Zheng; Chen, Zhong-Sheng; Yang, Yong-Min; Xu, Hai-Long
• Is Part Of: Mechanical systems and signal processing. Volume 81(2016)
• Page Start: 250
• Page End: 258
• Publication Date: 2016-12-15
• Subjects: Sparse representation -- Blade tip-timing measurement -- Blade vibration monitoring -- Under-sampled signal reconstruction -- Spectrum-blind signal; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.03.020
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Severe blade vibrations may reduce the useful life of the high-speed blade. Nowadays, non-contact measurement using blade tip-timing (BTT) technology is becoming promising in blade vibration monitoring. However, blade tip-timing signals are typically under-sampled. How to extract characteristic features of unknown multi-mode blade vibrations by analyzing these under-sampled signals becomes a big challenge. In this paper, a novel BTT analysis method for reconstructing unknown multi-mode blade vibration signals is proposed. The method consists of two key steps. First, a sparse representation (SR) mathematical model for sparse blade tip-timing signals is built. Second, a multi-mode blade vibration reconstruction algorithm is proposed to solve this SR problem. Experiments are carried out to validate the feasibility of the proposed method. The main advantage of this method is its ability to reconstruct unknown multi-mode blade vibration signals with high accuracy. The minimal requirements of probe number are also presented to provide guidelines for BTT system design. Highlights: A sparse representation mathematical model for blade tip-timing signals is built. An unknown multi-mode blade vibration reconstruction algorithm is presented. The minimal number of probes depends on the sparsity of blade vibration signal. Sampling patterns affect the performance of reconstruction.