大口径火炮故障分析与故障预测技术研究
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摘要
对于执行高强度训练和作战任务的大口径火炮而言，故障预测技术是实现预知维修，提高火炮战备完好性和任务成功性的有效手段。本文结合军队重点科研项目“大口径火炮动力学仿真及故障预测系统”，以大口径火炮为研究对象，以未来任务段内系统的动态特性为基础，运用先进的动态模糊综合评判理论和多Agent并行推理技术等多学科的交叉与融合，在对大口径火炮进行详细故障分析的基础上，系统、深入地开展了故障预测技术理论与应用的研究，建立了科学合理的故障预测模型，开发了相应的软件系统。
论文的主要研究内容包括以下几个方面：
一、针对部队目前维修保障工作存在的现实问题，基于现代战争对装备执行任务成功性的要求，提出了故障预测技术的基本概念和基于故障预测技术的装备预知维修策略。对大口径火炮进行了详细系统的故障统计分析和故障树分析，构建了完整的故障树，作为故障预测中明确预测对象、寻求推理路径和获取预测知识的基础。
二、针对大口径火炮故障预测的特点，首次提出了基于动态模糊综合评判和多Agent并行诊断的正反向混合推理的故障预测模型，由基于动态模糊综合评判的正向推理生成候选故障集，基于多Agent并行诊断的反向推理进一步确认实际发生的故障现象、找出故障原因及对应的故障部件，将正向推理与反向推理、不同的知识表达方式和推理机制融合于同一推理框架中，解决了复杂系统中定量类故障和定性类故障混合的多故障综合评判与预测的难题。
三、针对传统模糊综合评判方法用于故障预测存在的不足，首次将变权引入故障预测，提出了动态隶属度的概念和动态模糊综合评判方法。通过权重的动态变化和建立动态模糊关系矩阵描述故障的产生和发展过程，解决了火炮在执行不同训练和作战任务过程中的故障预测问题。
四、针对BDI Agent模型用于动态知识管理的不足，将知识融入Agent，提出了BDIK Agent模型，建立了多Agent并行推理结构，分析了管理Agent、诊断Agent和决策Agent的基本职能。构造了基于规则、事例、模糊神经网络和故障树四类常用的诊断Agent。提出了基于虚拟样机的知识获取机制，实现了深层知识的浅层化。基于多层次混合分解策略和合同网协议，阐述了诊断任务的分解和分配过程，给出了相应的招投标过程和算法描述。
五、采用竞争和合作描述多诊断Agent的协同求解。通过竞争，决策Agent首先找出相对最优解：通过合作(冲突消解)，生成最终求解结果，并基于相关回溯实现
The fault forecast technology is an effective means to improve the combat readiness and mission reliability for large caliber guns that often carry out high-strength training and battle tasks. This paper combines with the army's key scientific research task-"Dynamic Simulation and Fault Forecast for Large Caliber Guns", takes large caliber guns as research objects, adopts the integration of advanced dynamic fuzzy comprehensive evaluation theory and multi-Agent synchronous reasoning technology, and investigates the theory of fault forecast technology and its application on large caliber guns systematically and deeply. Based on the system's dynamic characters in future tasks, a scientific fault forecast model is established and an application software package is developed.
The main contents involved in this paper are as follows:
1. The deficiencies of traditional maintenance technologies are pointed out. Considering the request of modern war to equipment's mission reliability, the definitions of fault forecast technology and the predictive maintenance strategy based on fault forecast technology are put forward. The fault statistic analysis and the fault tree analysis are performed for large caliber guns. The fault trees of large caliber guns are established, which are the basis to ascertain forecast objects, seek reasoning paths and obtain forecast knowledge.
2. Considering the characteristics of large caliber guns, a hybrid-reasoning fault forecast model integrated with dynamic fuzzy comprehensive evaluation and multi-Agent synchronous reasoning is firstly presented. The forward reasoning process based on dynamic fuzzy comprehensive evaluation creates a candidate fault set. And the backward reasoning process based on multi-Agent synchronous reasoning confirms the actual faults and find out the corresponding fault reasons and fault components. The model combines forward reasoning and backward reasoning, different knowledge representations and reasoning mechanisms into a uniform reasoning framework, and resolve the puzzles of multi-fault comprehensive evaluation and the mixture fault forecast of quantitative and qualitative faults in complex systems.
3. Aiming to the deficiencies of traditional fuzzy comprehensive evaluation method, the dynamic weight coefficient is adopted into fault forecast. And the definition of dynamic membership and dynamic fuzzy comprehensive evaluation method are put forward. The fault emergence and development process is reflected by the dynamic variety and the
论文的主要研究内容包括以下几个方面：
一、针对部队目前维修保障工作存在的现实问题，基于现代战争对装备执行任务成功性的要求，提出了故障预测技术的基本概念和基于故障预测技术的装备预知维修策略。对大口径火炮进行了详细系统的故障统计分析和故障树分析，构建了完整的故障树，作为故障预测中明确预测对象、寻求推理路径和获取预测知识的基础。
二、针对大口径火炮故障预测的特点，首次提出了基于动态模糊综合评判和多Agent并行诊断的正反向混合推理的故障预测模型，由基于动态模糊综合评判的正向推理生成候选故障集，基于多Agent并行诊断的反向推理进一步确认实际发生的故障现象、找出故障原因及对应的故障部件，将正向推理与反向推理、不同的知识表达方式和推理机制融合于同一推理框架中，解决了复杂系统中定量类故障和定性类故障混合的多故障综合评判与预测的难题。
三、针对传统模糊综合评判方法用于故障预测存在的不足，首次将变权引入故障预测，提出了动态隶属度的概念和动态模糊综合评判方法。通过权重的动态变化和建立动态模糊关系矩阵描述故障的产生和发展过程，解决了火炮在执行不同训练和作战任务过程中的故障预测问题。
四、针对BDI Agent模型用于动态知识管理的不足，将知识融入Agent，提出了BDIK Agent模型，建立了多Agent并行推理结构，分析了管理Agent、诊断Agent和决策Agent的基本职能。构造了基于规则、事例、模糊神经网络和故障树四类常用的诊断Agent。提出了基于虚拟样机的知识获取机制，实现了深层知识的浅层化。基于多层次混合分解策略和合同网协议，阐述了诊断任务的分解和分配过程，给出了相应的招投标过程和算法描述。
五、采用竞争和合作描述多诊断Agent的协同求解。通过竞争，决策Agent首先找出相对最优解：通过合作(冲突消解)，生成最终求解结果，并基于相关回溯实现
The fault forecast technology is an effective means to improve the combat readiness and mission reliability for large caliber guns that often carry out high-strength training and battle tasks. This paper combines with the army's key scientific research task-"Dynamic Simulation and Fault Forecast for Large Caliber Guns", takes large caliber guns as research objects, adopts the integration of advanced dynamic fuzzy comprehensive evaluation theory and multi-Agent synchronous reasoning technology, and investigates the theory of fault forecast technology and its application on large caliber guns systematically and deeply. Based on the system's dynamic characters in future tasks, a scientific fault forecast model is established and an application software package is developed.
The main contents involved in this paper are as follows:
1. The deficiencies of traditional maintenance technologies are pointed out. Considering the request of modern war to equipment's mission reliability, the definitions of fault forecast technology and the predictive maintenance strategy based on fault forecast technology are put forward. The fault statistic analysis and the fault tree analysis are performed for large caliber guns. The fault trees of large caliber guns are established, which are the basis to ascertain forecast objects, seek reasoning paths and obtain forecast knowledge.
2. Considering the characteristics of large caliber guns, a hybrid-reasoning fault forecast model integrated with dynamic fuzzy comprehensive evaluation and multi-Agent synchronous reasoning is firstly presented. The forward reasoning process based on dynamic fuzzy comprehensive evaluation creates a candidate fault set. And the backward reasoning process based on multi-Agent synchronous reasoning confirms the actual faults and find out the corresponding fault reasons and fault components. The model combines forward reasoning and backward reasoning, different knowledge representations and reasoning mechanisms into a uniform reasoning framework, and resolve the puzzles of multi-fault comprehensive evaluation and the mixture fault forecast of quantitative and qualitative faults in complex systems.
3. Aiming to the deficiencies of traditional fuzzy comprehensive evaluation method, the dynamic weight coefficient is adopted into fault forecast. And the definition of dynamic membership and dynamic fuzzy comprehensive evaluation method are put forward. The fault emergence and development process is reflected by the dynamic variety and the
引文
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