基于动力学模型的柔性加工设备机电系统状态监测与故障预警

Condition Monitoring and Fault Prediction of MechanicalElectronic Systemsof Flexible Machining Devices Based on Dynamics Model

在建立的加工设备机电系统精确动力学模型及对模型参数实时辨识的基础上［１］，建立柔性加工设备机电系统的状态监测模型，包括ＢＡＹＥＳ故障检测、突发故障检测及故障分析与定位等；同时利用Ｋｕｌ－ｂａｃｋ信息距离和多层递阶ＡＲ模型实现系统的状态与故障预警。在加工设备上的实验表明模型可行有效，它能排除柔性多变工况、时变性及随机干扰对监测与预警功能的影响，提高其鲁棒性和自适应能力。

Based on the dynamics models and their identification of mechanicalelectronic systems of flexible machining devices, we have built up a real condition monitoring model which includes BAYES fault detection, abrupt fault detection and fault analysis&location. Utilizing the Kullback information distance and the multilevel hierarchical AR model, we have realized the prediction and alarming of faults of flexible machining devices. The experiments on the JCS020 Machine Center have indicated the models are feasible and valid.They can properly eliminate the influence of the changeful machining mode and process, the factors of timevarying and random disturbances on the condition monitoring , fault diagnosis and prediction, and greatly improve robustness and adaptability of monitoring and diagnosis in the FMS environments.