The twin-screw pump (TSP) is widely used in naval engineering for seawater transportation and treatment. However, the diverse composition of seawater, often containing impurities, poses a risk of screw corrosion and jamming of clearances. Detecting the compact internal structure's clearance between rotor and stator (GAPR) proves challenging. To address these challenges, a novel approach combining chaos theory's reconstructive phase space technique was proposed for processing pressure pulsation signals. The research found that only specific monitoring points displayed chaotic characteristics in pressure pulsation signals. The attractor structure complexity increased with GAPR changes, while the chaotic features decreased. Nonlinear analysis of pulsation signals at different GAPR values allowed determining reasonable ranges. Applying support vector classification algorithm based on chaotic dynamics achieved an impressive 89% accuracy in identifying GAPR values. This study offers practical insight for TSP fault detection and operational optimization, holding significance in both theory and practice.

中文翻译：

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不同定子间隙下船用双螺杆泵压力脉动非线性特性数值模拟与实验研究

双螺杆泵（TSP）广泛应用于海军工程中的海水输送和处理。然而，海水成分多样，通常含有杂质，存在螺杆腐蚀和间隙堵塞的风险。检测紧凑内部结构的转子和定子之间的间隙 (GAPR) 具有挑战性。为了解决这些挑战，提出了一种结合混沌理论的重构相空间技术来处理压力脉动信号的新方法。研究发现，只有特定监测点的压力脉动信号表现出混沌特征。随着GAPR的变化，吸引子结构复杂度增加，而混沌特征减少。对不同 GAPR 值下的脉动信号进行非线性分析可以确定合理的范围。应用基于混沌动力学的支持向量分类算法在识别 GAPR 值方面实现了令人印象深刻的 89% 准确率。本研究为TSP故障检测和运行优化提供了实用见解，具有理论和实践意义。