往复机械振动的双树复小波包分析与在线异常检测方法研究

项目名称： 往复机械振动的双树复小波包分析与在线异常检测方法研究

项目编号： No.51305454

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 机械、仪表工业

项目作者： 吴定海

作者单位： 中国人民解放军军械工程学院

项目金额： 24万元

中文摘要： 机械状态监测面临的先验知识不足、故障样本稀缺、故障模式不完备，是制约在线监测与故障诊断精度提高的根本原因。针对大型复杂往复机械所监测的振动信号具有干扰多、非平稳、耦合性、强周期性等特点，难以直接实现准确诊断。本项目拟研究一种基于数据驱动的往复机械在线异常检测新方法，实现及时发现并进行故障报警，课题重点分析高维核特征空间样本分布问题和最优分类超平面思想，建立新型单类支持向量机异常检测模型；围绕异常检测模型，研究双树复小波包平移不变性和稀疏分解能力，利用其提高信号降噪和多元故障特征的综合表征能力；针对异常检测面临的特征提取、特征选择和分类器参数耦合难题，提出一种联合多目标优化方法；分析新增样本对异常检测模型的影响，提出一种在线增量式训练算法，实现异常检测的自学习和通用性。本项目研究成果可拓展到其他状态监测与故障诊断领域，为机械早期和复合等故障检测识别难题提供一套有效、实用的新方法。

中文关键词： 往复机械；状态监测；单分类支持向量；异常检测；双树复小波包

英文摘要： Because of lack of prior knowledge, fault samples and fault mode of machinery condition monitoring, the precision of fault diagnosis is hard to improve. According to the characteristics of multi interference, non-stationary, coupling and periodicity of large and complex reciprocating machine vibration signal, accurate diagnosis is hard to realize. So a method of anomaly detection which based on data-driven of reciprocating machinery is proposed in this project. The new type one-class support vector machine anomaly detection is established to find the fault and alarm in time before the problem of sample distribution of high dimensional kernel feature space and optimum classification hyper plane is analysized; The machinery vibration signal process method of dual-tree complex wavelet packet transform which has the advantage of translation invariance and sparse decomposition is used to improve the signal de-noising and multivariate feature extraction effect. Then a combined multi-objective optimization algorithm will be studied to solve the coupling problem of feature extraction, feature selection and classifier parameters; After analysis new added samples effect of anomaly detection model, a on-line incremental train method is proposed to made the anomaly detection model has the ability of self-learning and genera

英文关键词： reciprocating machinery；condition monitoring；one-class support vector machine；anomaly detection；dual-tree complex wavelet packet transform