基于脆弱性的制造设备故障智能诊断与维护

制造设备故障智能诊断与维护是保障制造系统安全运行的重要手段。为准确地诊断制造设备的健康状态、识别设备故障的关键因素,建立高效的健康维护系统,提出了基于脆弱性的设备故障智能诊断与维护方法。该方法将考虑脆弱性的设备故障智能诊断与维修决策模块嵌入到设备的过程控制系统(Process control system,PCS)中,它基于系统脆弱性的定义和性能劣化理论建立了设备脆弱性评估模型实时判断设备的脆弱状态,利用非线性核映射方法实时监测制造设备的运行参数是否超出预设边界,建立设备参数的高斯核函数模型准确识别故障的关键因素,将设备的脆弱性状态与维护模式相结合建立维修决策模型避免维修过度和维修不足。以某机器人的伺服系统为例,证实了所提方法能有效提高故障诊断效率、智能化诊断故障因素,优化设备维修决策。

Fault Diagnosis and Maintain of Manufacturing Equipment Based on Vulnerability

Intelligent fault diagnosis and maintenance are the important tools to guarantee the healthy operation of the manufacturing system. In order to accurately diagnose the health status of manufacturing systems,identify key factors of equipment failures, and establish an efficient health maintenance system, a vulnerability-based method is proposed for the intelligent diagnosis and maintenance of equipment failures. In this method,the module of intelligent diagnosis and maintenance decision optimization,which the vulnerability of the manufacturing equipment is considered,is embedded into the Process control system(PCS) of the equipment. In order to judge the vulnerability state of the equipment timely, the definition of vulnerability and degradation theory are combined to establish a vulnerability assessment model. The Non-linear kernel mapping method is used to implement real-time monitoring of the operating parameters of manufacturing equipment,a Gaussian kernel function model of equipment parameters is established to accurately identify the key factors of failure. To avoid over-repair and under-maintenance,the equipment vulnerability status and maintenance mode are combined together to establish maintenance decision model. The results for a robot servo system indicate that the proposed method can improve the efficiency of fault diagnosis effectively,identify fault factors accurately, and optimize maintenance decision.