کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
559154 1451861 2016 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Blade resonance parameter identification based on tip-timing method without the once-per revolution sensor
ترجمه فارسی عنوان
شناسایی پارامتر رزونانس تیغه بر اساس روش زمان نوشتن بدون سنسور یکبار در هر انقلاب
کلمات کلیدی
تیغه نوک زمان، لرزش تیغه شناسایی پارامتر
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
چکیده انگلیسی


• This file is a short collection of bullet points that convey the core findings of the article:
• Blade number identification method without once-per revolution (OPR) signal is presented, which identifies the blade number a tip-timing signal belongs to.
• Blade resonance parameter identification without OPR signal is presented.
• Theoretic error of the method is analyzed and corrected.
• Engine order identification method without OPR signal is presented, so blade resonance parameter based on tip-timing method is achieved.

Blade tip-timing is the most effective method for blade vibration online measurement of turbomachinery. In this article a synchronous resonance vibration measurement method of blade based on tip-timing is presented. This method requires no once-per revolution sensor which makes it more generally applicable in the condition where this sensor is difficult to install, especially for the high-pressure rotors of dual-rotor engines. Only three casing mounted probes are required to identify the engine order, amplitude, natural frequency and the damping coefficient of the blade. A method is developed to identify the blade which a tip-timing data belongs to without once-per revolution sensor. Theoretical analyses of resonance parameter measurement are presented. Theoretic error of the method is investigated and corrected. Experiments are conducted and the results indicate that blade resonance parameter identification is achieved without once-per revolution sensor.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Mechanical Systems and Signal Processing - Volumes 66–67, January 2016, Pages 625–639
نویسندگان
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