Condition monitoring data of joints in a mechanism contains enormous useful information, and can comprehensively improve the wear prediction accuracy. However, condition data of the joints is sometimes hard to obtain due to technical reasons or cost reasons, especially for some complicated mechanical systems. To obtain the real-time wear data of joints in a mechanism with multiple joints, an ANFIS-based (adaptive-network-based fuzzy inference system) joints clearance size prognostic method is developed based on monitored motion outputs of the mechanism. Then, a framework for wear prediction based on multi-body dynamics theory is proposed to predict joints wear more accurately. In the framework, the Archard’s wear model is used. To reduce the uncertainty in the wear coefficient, wear coefficient is treated as a random variable, then a Bayesian updating process is implemented according to the wear data from the ANFIS-based method. The proposed framework is validated using wear experiments of a lock mechanism with three joints in a cabin door. The results show the prediction error is within 3%.

机构中的关节状态监测数据包含大量有用信息，可以全面提高磨损预测精度。然而，由于技术原因或成本原因，有时难以获得接头的状态数据，尤其是对于某些复杂的机械系统。为了获得具有多个关节的机构中关节的实时磨损数据，基于监视的机构运动输出，开发了基于ANFIS（基于自适应网络的模糊推理系统）的关节间隙尺寸预测方法。然后，提出了一种基于多体动力学理论的磨损预测框架，以更准确地预测关节磨损。在框架中，使用了Archard的磨损模型。为了减少磨损系数的不确定性，将磨损系数视为随机变量，然后根据基于ANFIS的方法的磨损数据执行贝叶斯更新过程。所提出的框架通过使用机舱门中具有三个接头的锁定机构的磨损实验进行了验证。结果表明预测误差在3％以内。