面向复杂机电系统多源状态信息的运行可靠性评估方法研究

项目名称： 面向复杂机电系统多源状态信息的运行可靠性评估方法研究

项目编号： No.51205230

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 机械工程学科

项目作者： 陈保家

作者单位： 三峡大学

项目金额： 25万元

中文摘要： 在分析复杂机电系统可靠性时，传统可靠性评估方法在描述时间、动态过程等方面存在困难，个体针对性不强；退化可靠性评估方法需要了解设备的退化机理，或对状态参数的退化路径和概率分布做过多假设。针对以上问题，提出了基于多源状态信息的运行可靠性评估方法。通过构建高效的LMD分解算法和RBSD低共振分量解调分析，提取复杂信号调制特征；将具有准确定义和明显趋势性变化的信号参数作为状态特征指标，为可靠性评估提供数据基础；提出PHM、PCM、Logistic回归模型和Logistic+PCM复合模型分别解决大样本和小样本条件下的运行可靠性分析问题；根据机电系统动力学、摩擦学理论和退化失效原理，找出影响系统可靠性的关键因素；在线、动态、主动地评估预测关键设备性能退化过程，提高其可靠性分析与评估的可信度；为设备的管理和维护提供科学的决策依据，尽可能减少和消除事故，节约维修费用；丰富和发展可靠性评估理论。

中文关键词： 复杂机电系统；多源状态信息；退化机理；故障诊断；运行可靠性评估

英文摘要： According to the reliability estimation of complex electromechanical system，the traditional reliability estimation methods are limited for some reasons, such as the difficulties in time-dynamic process description and non-individual characteristics. Degradation-based reliability estimation methods are required to know the professional background knowledge and degradation mechanism, or make assumptions about the degradation tracks and the probability distribution of the state features. To solve the above problems, the operational reliability estimation methods for complex electromechanical system based on multi-source condition information are proposed. The efficient LMD decomposition algorithm is constructed and the low resonance component demodulation analysis of RBSD is adopted to extract the complex signal modulation characteristics. Some salient features are selected from signal parameters which having a precise definition and a clear trend change as data base of the reliability assessment. The PHM, PCM, logistic regression and Logistic+PCM composite model are respectively proposed to implement operational reliability under the conditions of the large sample and small sample. The key factors affecting the system reliability are identified based on the analysis of electromechanical system dynamics, tribology

英文关键词： complex electromechanical system；multi-source condition information；degradation mechanism；fault diagnosis；operational reliability estimation