舰船动力装置振动信号的超小波自适应分解与多重损伤特征定量识别

项目名称： 舰船动力装置振动信号的超小波自适应分解与多重损伤特征定量识别

项目编号： No.51275384

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 机械、仪表工业

项目作者： 訾艳阳

作者单位： 西安交通大学

项目金额： 82万元

中文摘要： 本项目从舰船的高可靠性、高安全性、超长寿命、可监测、易维护发展需求出发，针对其动力装置多重损伤信号特征提取的难题，将多小波变换和双树复小波变换有机集成，提出一种自适应超小波构造方法与最优分解策略；提出具有性能差异的自适应超小波基函数构造原理和方法，实现与多重损伤特征匹配的超小波基函数向量构造；提出基于峭度最大和熵最小的超小波最优分解原理与多重特征解耦分离方法，优化超小波滤波器组结构，提高噪声抑制能力与耦合特征解耦分离能力；提出基于信号多维测度的损伤定量评价指标体系构建方法，实现损伤类型的分类和损伤程度的定量识别。研究成果可为复杂机械系统故障预示与运行安全评估提供有效的分析手段，将有效提高舰船动力装置服役性能和预防重大事故发生，具有重要的理论意义与工程应用价值。

中文关键词： 舰船动力装置；振动信号；多重损伤特征；超小波；自适应分解

英文摘要： This research project aims to seek improvements in the following aspects. According to the high reliability, high security, long service time, monitor-ability and easy maintenance requirements of ships, a construction strategy and optimal decomposition scheme of adaptive super-wavelets which is based on organic integration of multi-wavelets and the dual-tree complex wavelets are put forward to solve the multi-damage feature extraction problem of ship power-plant. The theoretical fundamental and construction strategy of super-wavelets characteristic of variable feature matching performance are investigated for obtaining vector super-wavelets which is capable of matching multi-damage features. Bases on maximization of kurtosis and minimization of entropy, decomposition scheme and multi-feature decoupling method are proposed for optimizing filter-bank structure of super-wavelets as well as improving its noise suppression ability and coupling feature separation performance. A quantitative damage assessment index system, based on multi-dimensional measure of signal, is constructed for realization of both damage pattern classification and quantitative recognition of damage severity. The perspective research achievements are promising for providing effective measures for fault prognosis and their operation security ass

英文关键词： ship power-plant；vibration signal；multiple damage features；super-wavelets；adaptive decomposition