基于群体智能的柴油机性能评估与故障预测研究

项目名称： 基于群体智能的柴油机性能评估与故障预测研究

项目编号： No.51305089

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： 王忠巍

作者单位： 哈尔滨工程大学

项目金额： 24万元

中文摘要： 柴油机作为最广泛应用的动力机械在国民经济各领域中发挥着重要作用，柴油机监测诊断技术是实现故障早期预报、确保柴油机安全、高效运行的有效手段。机车用、船用等大功率柴油机均为多缸柴油机，各气缸的结构和功能一致、工作条件相同，彼此协调共同向主轴输出扭矩，其技术状态能够切实反映整机的健康状况。根据柴油机特有的结构和运行特点，本研究提出利用故障所引起的气缸间性能差异实现柴油机监测诊断的新方法。研究内容包括：将柴油机"气缸群"抽象为"蚁群、蜂群"，基于群体智能聚类方法实现"气缸群"性能的自组织横向比较，发现异常气缸及相关运行参数；在此基础上，建立多变量灰色MGM模型，定量预测潜在故障的劣化趋势；采用贝叶斯网络融合"气缸群"聚类分析结果与领域专家知识，准确诊断柴油机故障类型及原因。本课题旨在探索故障样本数据缺乏条件下，实现柴油机故障预测的新途径，研究成果对提高柴油机动力系统的安全性、经济性具有重要意义。

中文关键词： 柴油机；故障预测；群体智能；蚁群算法；贝叶斯网络

英文摘要： The diesel engine, as the most widely used power machinery, plays an important role in all fields of national economy. The diesel engine detecting and diagnosing technique provides an effective means of forecasting faults in the early stage to ensure the safe and efficient operation of the engine. Big power diesel engines used for marine ships and locomotives are multi-cylinder engines with the similar structure, function and working conditions, which together provide torsion to the crank. The technical state of the cylinders reflects the health condition of the engine factually. Based on the diesel structure and operation character, the project proposes a novel diesel diagnosing method utilizing the cylinder performance discrepancy. The research includes: the diesel multi-cylinder is abstracted to ant swarm or bee swarm, then based on the Swarm Intelligence technology, the cylinder swarm operation character is compared to find the abnormal one with its relative operation parameters; on the bases multi-variable grey MGM model is adopted to forecast the transformation trend of diesel latency fault; Blending the cluster analysis results and diesel expert knowledge with the Bayesian Network, then the fault types and fault reasons will be got exactly. The resear project aims at providing novel diesel fault diagnosis

英文关键词： Diesel engine；Fault prediction；Swarm intelligence；Colony algorithm；Bayesian network