Abstract
A turbomachine is a fundamental engineering apparatus meant to transfer energy between a rotor and a fluid. Turbomachines are the core of power generation in many engineering applications such as electric power generation plants, aerospace, marine power, automotive etc. Their relevance makes them both mission critical and safety critical in many fields. To foster reliability and safety, then, continuous monitoring of the rotor is more than desirable. One promising monitoring technique is, with no doubt, the Blade Tip-Timing, which, being simple and non-invasive, can be easily implemented on many different rotors. Blade Tip-Timing is based on the recording of the time of arrival of the blades passing in front of a probe located at a fixed angular position. The non-contact nature of the measurement prevents influences on the measured vibration, that can be recovered for all the blades simultaneously, possibly even online. In this regard, a novel algorithm is presented in this paper for obtaining a good estimate of the vibration of the blades with minimum system complexity (i.e., only one Blade Tip-Timing probe) and minimum computational effort, so to create a simple vibration monitoring system, potentially implementable online. The methodology was tested on a dataset from a SAFRAN turbomachine made available during the Surveillance 9 international conference for a diagnostic contest.
Highlights
Turbomachines are the core of power generation in many engineering applications such as electric power generation plants, aerospace, marine power, automotive, etc
A damaged blade, having a reduced cross section, features different dynamic behavior with respect to a healthy one. Starting from these considerations, the research challenge is that of proposing a novel diagnostic system for real-time online monitoring derived as an improvement to the algorithm in [13], able to solve the issue of non-uniform error for the different blades, while featuring a reduced computational burden
The here proposed improved algorithm will be compared to the reference algorithms in [8,13] (i.e., the presumed algorithm of Blade Tip Time Averaging—Ives (1986), which was impossible for the authors to find) on the dataset from the Surveillance 9 contest, described
Summary
A turbomachine is a fundamental engineering apparatus meant to transfer energy between a rotor and a fluid. The main error sources in the BTT measurement chain are recognized to be: clock resolution (i.e., the time of arrival—ToA—of the blades are compared to an internal clock having a given resolution), sensors vibration (i.e., the tip-timing and OPR probe are usually mounted on the casing, which vibrates during the turbomachine operation), geometric errors of the blade mounting (i.e., the blades will never be perfectly equispaced), non-stationarity (i.e., some algorithms assume uniform rotational speed, but speed fluctuations or fast accelerations may lead to additional error). A damaged blade, having a reduced cross section, features different dynamic behavior with respect to a healthy one Starting from these considerations, the research challenge is that of proposing a novel diagnostic system for real-time online monitoring derived as an improvement to the algorithm in [13] (i.e., single probe tip timing, without root and OPR sensors), able to solve the issue of non-uniform error for the different blades, while featuring a reduced computational burden.
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