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作 者:王永坚[1] 吴怡婷 魏旻 蔡杭溪 ZHAO Kai WANG Yongjian;WU Yiting;Wei Min;CAI Hangxi;ZHAO Kai(Marine Engineering Institute,Jimei University,Xiamen 361021,Fujian,China)
出 处:《船舶工程》2022年第10期71-79,共9页Ship Engineering
基 金:福建省自然科学基金(2020J01687);厦门市海洋发展局科技项目(21CZB014HJ08)。
摘 要:针对船用中高速柴油机缸套-活塞环振动信号存在的非线性、非平稳性问题,利用振动信号辨识故障,综合运用集成经验模态分解(EEMD)和深度卷积神经网络(DCNN)的信号处理优势,提出一种将EEMD与DCNN相融合的缸套-活塞环故障诊断方法。通过开展正常状态、人为设置缸套过度磨损0.25 mm和0.50 mm、活塞1环断裂等4种状态的试验,基于EEMD对采集的振动信号经进行分解,得到本征模态函数(IMF),以峭度为评价指标,选取各状态下的IMF分量,输入DCNN模型中,自学习提取故障特征,经Softmax分类器识别和分类故障,实现智能化故障诊断。试验结果表明:与支持向量机(SVM)等其他诊断方法相比,该方法能更准确、有效地识别缸套-活塞环故障类别。In order to solve non-linear and non-stationary problem for vibration signal causing by marine medium-high speed diesel engine cylinder liner-piston rings, make full use of vibration signal to identify faults,comprehensive application the advantages of ensemble empirical mode decomposition(EEMD) and deep convolutional neural network(DCNN) in signal processing, a method for fault diagnosis of cylinder liner-piston rings is proposed which integrated EEMD with DCNN. Through normal state, artificial setting excessive wear of cylinder liner 0.25 mm/0.50 mm, first piston ring fracture and other four fault state’s experiments, EEMD is used to decompose the collected vibration signal to obtain the intrinsic mode function(IMF), then using kurtosis as the evaluation index to select IMFs, These IMFs are inputed to DCNN model, then automatic fault features are extracted by self-learning, Finally, the intelligent fault diagnosis is implemented by softmax classifier to recognize the faults. The experimental results show that this method can identify and classify fault modes of cylinder liner-piston rings more effectively and accurately, compared with other diagnostic methods as support vector machine(SVM).
关 键 词:船用中高速柴油机 缸套-活塞环 集成经验模态分解(EEMD) 深度卷积神经网络(DCNN) 故障诊断
分 类 号:U664.51[交通运输工程—船舶及航道工程]
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