Xu Feixiang, Liu Xinhui, Chen Wei, Zhou Chen, Cao Bingwei
School of Mechanical Science and Engineering, Jilin University, Changchun 130022, China.
Sensors (Basel). 2018 Feb 28;18(3):729. doi: 10.3390/s18030729.
This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.
本文提出了一种基于本体的故障诊断方法，该方法克服了装载机复杂故障诊断知识理解困难的问题，并为所有装载机的故障诊断提供了一种通用方法。该方法包括以下几个部分：（1）提出了一种基于本体的故障诊断模型，以实现装载机故障诊断知识的集成、共享和重用；（2）结合本体，引入基于案例推理（CBR），通过特征选择、案例检索、案例匹配和案例更新四个步骤实现有效、准确的故障诊断；（3）为弥补CBR方法因缺乏相关案例而存在的不足，通过构建语义网规则语言（SWRL）规则，提出了基于本体的基于规则推理（RBR）。还开发了一个应用程序来实现上述方法，以协助查找装载机的故障原因、故障位置和维护措施。此外，通过分析一个案例研究对该程序进行了验证。
