Abstract
The small leak in the propulsion system pipeline of the sounding rocket is prone to occur in the connections because of the screw thread loosening. Due to economic and technical bottleneck, the traditional soap bubble method is widely used in practice to evaluate whether existing a leak or not by visually observing the bubble’s size and numbers. Thus doing so will result in the low inspection efficiency and high cost. Using acoustic emission (AE) techniques, this paper presents an experimental study on small leak detection on the screw thread connection in the propulsion system pipeline of sounding rocket. The time and frequency characteristics of the corresponding small leak AE signals are investigated. After characteristic indices extraction and selection, the multi-class support vector machines (MCSVM)-based leak rates recognition algorithm in One–vs–All (OVA) is proposed. It has been validated that, for the propulsion system pipeline of the sounding rocket, the dominant characteristic frequency band of the small leak AE signals induced by screw thread loosening concentrates on 35–45 kHz. The proposed optimal OVA SVM models can achieve good classification accuracy of >98% by using the characteristic index set {Envelope area, standard deviation (STD), root-mean-square (RMS), Energy, Average frequency} and Gaussian Radial Basis Function (RBF) kernel function. The drastic drops in the false alarm attribute to use the combination of time- and frequency-domain characteristic indices. Especially, once adding the “Envelope area” into the characteristic index set, the classification accuracies of the OVA SVM models are further improved significantly regardless of the effect of kernel functions.
Highlights
Aluminum alloy pipes with screw thread connections are widely used for the propulsion system pipeline of the sounding rocket
Through training of experimental data in cases C1 ∼ C5, it is can be seen that Gaussian Radial Basis Function (RBF) kernel function and the characteristic index combination {CI1: Envelope area, CI2: standard deviation (STD), CI3: RMS, CI4: Energy, CI5: Average frequency} can be used to construct the optimal the support vector machine (SVM) model in OVA for detecting the small leak acoustic emission (AE) signal generated in the propulsion system pipeline of the sounding rocket based on pattern recognition
This paper presents an experimental investigation on AE-based leak detection of a propulsion system pipeline of the sounding rocket subject to failure of the screw thread loosening
Summary
Aluminum alloy pipes with screw thread connections are widely used for the propulsion system pipeline of the sounding rocket. It is well known that the leak in this type of pipeline is prone to occur in the connections because of the screw thread loosening. This kind of small leak is very drowned by the complicated noise. It is much harder to timely identify small leaks [1]. In the actual assembly process of the propulsion system pipeline of the sounding rocket in China, the quality inspector usually spreads the soap solution on the screw thread connections to evaluate whether existing a leak or not. The leak rates can be calculated according to the bubble’s
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