【摘要】 随着社会经济的快速发展，现代工业逐渐呈现出研究对象复杂化、应用手段信息化和生产方式多元化的发展趋势。机械故障诊断作为现代工业重要的研究领域之一，由于故障机理研究不足和可参考知识匮乏等问题，仍然存在一系列技术瓶颈。为应对上述问题，文中提出了知识驱动的机械设备故障诊断方案，主要包括知识构建和诊断流程两个部分。在知识构建方面，提出了领域知识图谱构建方法；在诊断流程方面，设计了一个通用的机械设备故障诊断流程，该流程包括故障问诊、故障定位、起因定位和故障维修指导4个步骤。目前，该方案已经在国内某大型挖掘机维修商实际落地应用，并对其进行了有效性验证，实验结果表明该方案提高了挖掘机故障诊断领域的知识化程度和智能化水平，并表现出了较高的准确性和实用性，后续将在工业界持续推广使用。更多还原

【Abstract】 With the rapid development of social economy, modern industry now presents a trend featuring complex research objects, informationalized application methods and diversified production modes.Industrial fault diagnosis, as one of the most important research areas in modern industry, is still facing a series of technical bottlenecks due to the complexity of mechanical equipment and the lack of referential knowledge.In order to solve the above problems, this paper proposes a knowledge-driven fault diagnosis scheme for mechanical equipment, which mainly includes two parts——knowledge construction and diagnosis process.In terms of knowledge construction, this paper presents a domain knowledge graph construction method.In terms of diagnosis process, this paper designs a general mechanical equipment fault diagnosis process consisting of four steps, fault inquiry, fault location, fault cause location and fault maintenance guidance.To date, the scheme has been actually applied in a large excavator maintenance provider in China, and its effectiveness has been verified.Experimental results indicate the scheme improves the know-ledge and intelligent level of excavator fault diagnosis and shows high accuracy and practicability.The application of the scheme will be further promoted in the industry.更多还原