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船用柴油机轴系不对中在线检测与自愈调控方法

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船用柴油机及其动力轴系受力状态复杂,结构装配、船体变形等影响相互耦合,常发生轴系不对中故障,易导致轴承严重磨损、联轴器断裂、发动机曲轴断裂等严重事故.受结构、工况等条件影响,传统的振动时频特征分析方法难以实现轴系不对中故障的准确定量检测,工频、二倍频特征频率也易与其他故障特征混叠并造成误判.基于不对中状态下的轴系结构形态特性,建立一种不对中形态特征计算模型,针对不对中形式、机械结构影响提出了在线检测方案;以柴油机输出轴为研究对象,建立6自由度故障模拟试验台,完成了不对中检测方法的试验验证;进一步提出一种轴系不对中故障自愈调控方案并完成试验验证,实现了轴系不对中故障的在线自愈调控.数据表明:基于轴系形态特征的不对中检测方法实现了不同工况条件下的不对中在线检测,准确率超过90%;伺服电缸驱动的自愈调控装置可在10 s内使轴系不对中量降低超过75%.
Online Detection and Self-healing Regulation Method of Marine Diesel Engine Shafting Misalignment
The force of a marine diesel and its shafting are complex.With effects between structure assembly and hull deformation,shaft misalignment often occurs,which can lead to serious accidents such as bearing wear,coupling fracture,and crankshaft fracture.Due to the influence of structural and operational conditions,traditional vibration time-frequency analysis methods struggle to accurately detect and quantify shafting misalignment.Additionally,the characteristic of power and double frequency is easy to be aliased with other fault features and causes misjudgment.Therefore,a model for misalignment morphological characteristics is developed based on the structural features of misaligned shafting.An online detection scheme is proposed to account for misalignment patterns and mechanical structure effects.The research focuses on the diesel output shaft,and a six-degree-of-freedom fault simulation test bench is established for experimental verification of the misalignment detection method.Furthermore,a self-healing control scheme for shafting misalignment faults is proposed and validated through experiments,enabling online self-healing control of such faults.The data demonstrates that the misalignment detection method based on shafting morphological characteristics can achieve online detection of misalignment under different working conditions,with an accuracy rate exceeding 90%.The self-healing control device driven by servo electric cylinder can reduce the misalignment of shafting by more than 75%within 10 s.

misalignmentself-healing regulationstructural morphological characteristicsonline detectionmarine diesel

张进杰、王怀磊、窦全礼、王子嘉、茆志伟

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北京化工大学高端机械装备健康监控与自愈化北京市重点实验室 北京 100029

潍柴动力股份有限公司 潍坊 261000

清华大学高端装备界面科技全国重点实验室 北京 100084

不对中 自愈调控 结构形态特性 在线检测 船用柴油机

2024

机械工程学报
中国机械工程学会

机械工程学报

CSTPCD北大核心
影响因子:1.362
ISSN:0577-6686
年,卷(期):2024.60(20)