This paper aims to build a diagnosis model of a lock mechanism system considering multiple joints wear. The lock mechanism is a complex mechanical system. Diagnosis is difficult because fault modes are not easy to identify. The diagnosis result is affected by the strong dependence of wear between each component. Besides, some lock mechanism’s wear detection data is challenging to acquire because of fewer sensors in some particular situations. For these problems, an improved Bayesian network-based fault diagnosis methodology considering component degradation is proposed to distinguish the fault types. An experiment of congeneric lock mechanisms is conducted, and wear data of the joints is obtained. The Bayesian networks model in which the dependence of components is not considered is established based on experimental data. Because the Bayesian network structure is affected by the strong dependence between components, the K2 algorithm is used to build the Bayesian network structure based on acquired wear data to obtain causality between components. The entire fault model is built by combining two established Bayesian networks. Three fault diagnosis cases are used to validate the accuracy and efficiency of the proposed model. A comparison is made between the improved diagnosis model and the model without considering dependence. Finally, the revolute joints are ranked by the established diagnostic model so that the weakest component can be identified.

本文旨在建立一种考虑多关节磨损的锁紧机构系统的诊断模型。锁定机构是一个复杂的机械系统。由于故障模式不容易识别，因此诊断很困难。诊断结果受每个组件之间强烈的磨损依赖性影响。此外，由于某些特定情况下的传感器较少，因此某些锁定机构的磨损检测数据很难获得。针对这些问题，提出了一种基于组件退化的改进贝叶斯网络故障诊断方法，以区分故障类型。进行了同类锁机构的实验，获得了接头的磨损数据。基于实验数据建立了不考虑组分依赖性的贝叶斯网络模型。由于贝叶斯网络结构受组件之间强烈依赖的影响，因此使用K2算法基于获取的磨损数据构建贝叶斯网络结构，以获取组件之间的因果关系。整个故障模型是通过组合两个已建立的贝叶斯网络建立的。使用三个故障诊断案例来验证所提出模型的准确性和效率。在不考虑依赖性的情况下，对改进的诊断模型与模型进行比较。最后，根据已建立的诊断模型对旋转关节进行排名，以便可以识别最弱的组件。整个故障模型是通过组合两个已建立的贝叶斯网络建立的。使用三个故障诊断案例来验证所提出模型的准确性和效率。在不考虑依赖性的情况下，对改进的诊断模型与模型进行比较。最后，根据已建立的诊断模型对旋转关节进行排名，以便可以识别最弱的组件。整个故障模型是通过组合两个已建立的贝叶斯网络建立的。使用三个故障诊断案例来验证所提出模型的准确性和效率。在不考虑依赖性的情况下，对改进的诊断模型与模型进行比较。最后，根据已建立的诊断模型对旋转关节进行排名，以便可以识别最弱的组件。