铰链磨损是影响飞机机构性能的重要因素,铰链磨损规律的预测对提高机构的寿命和可靠性水平具有重要意义。对于含多个铰链的复杂机构,由于载荷和运动规律的复杂性很难准确预测铰链的磨损深度。为此提出了一种基于机构运动输出监测数据的铰链磨损深度综合预测方法,将磨损系数作为随机变量,考虑由于加工、制造及机构运行环境等因素产生的不确定性。根据含间隙铰链机构的运动学模型推导建立了机构运动输出与多个铰链磨损深度的映射关系,根据易于获取的机构运动输出监测数据(如位移、角度等)得到了铰链在监测时刻的磨损状态。将该数据作为观测数据,通过贝叶斯理论更新磨损系数的分布信息。结果显示机构的运动输出监测数据越多,磨损系数后验分布就越接近实际。结合运动机构的多体动力学模型和Archard磨损模型预测铰链的磨损深度。通过飞机舱门锁机构中铰链的磨损试验对所提方法的有效性进行了验证,结果显示预测误差在6%左右。
Wear of joints is an important factor influencing the performance of a mechanism, and wear prediction is of great significance to the design and maintenance of a mechanism. For the multiple joints in a complex motion mechanism, it is difficult to predict the wear in the joints accurately due to the complexity of the load and motion. To solve this problem, this paper proposes an integrated wear prediction method based on the monitoring data of motion output. The wear coefficient is treated as a random variable in analysis of the uncertainty in the processing, manufacturing and running environment. The mapping relationship between the motion output and the wear depth of multiple joints is deduced according to the kinematic model of the mechanism, and then the wear state data of the joint can be obtained according to the easily obtained monitoring data of the motion output (such as displacement, angle, etc.). On the basis of the obtained wear state data of the joints, distribution information of the wear coefficient is updated based on the Bayesian theory. With more monitoring data, the posterior distribution of the wear coefficient is more realistic. Based on the multibody dynamics model and the Archard wear model, the wear depth of joints is predicted. Wear experiment of a lock mechanism in an aircraft cabin door shows that the prediction error is about 6%, verifying the effectiveness of the proposed method.
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