Data driven health monitoring and fault diagnosis of mechanical equipment
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摘要: 针对机械设备故障数据利用不完全、传统健康状态监控及故障诊断方法存在局限的问题，构建一种数据驱动的设备健康状态监控及故障诊断系统架构。提出一种基于数字孪生的健康状态监控方法，结合机械设备脆弱性监控健康状态。研究一种数据驱动的故障诊断方法，运用非线性核映射算法分析故障的历史数据，确定设备异常边界，通过分析相关实时数据确定设备是否异常及导致异常的关键因素。最后以某装配生产线机械手臂为例进行实证分析，结果表明，所提方法可有效识别导致设备异常的性能参数，提高了车间对机器故障的动态响应能力。Abstract: To address the incomplete utilization of mechanical equipment fault data and overcome the limitations of traditional health status monitoring and fault diagnosis methods, a data-driven system architecture for health monitoring and fault diagnosis was constructed. A health status monitoring method based on digital twins was proposed, in which equipment vulnerability was incorporated to monitor the health status. Furthermore, a data-driven fault diagnosis method was studied. By utilizing a nonlinear kernel mapping algorithm, historical fault data were analyzed to determine the abnormal boundary of the equipment. Through the analysis of real-time data, equipment abnormalities and the key factors leading to these abnormalities were identified. Finally, an empirical analysis was conducted taking a robotic arm on an assembly line as a case study. The results indicate that the proposed method can effectively identify the performance parameters leading to equipment abnormalities and improve the dynamic response performance of the workshop to machine failures.
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Key words:
- date-driven /
- mechanical equipment /
- health status /
- fault diagnosis /
- vulnerability
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图 1 数据驱动的机械设备健康状态监控及故障诊断系统架构
Figure 1. Architecture of data-driven mechanical equipment health status monitoring and fault diagnosis system
图 3 机械设备反脆弱性值与健康状态对应关系图
Figure 3. Correspondence between mechanical equipment anti-vulnerability value and health status
图 5 基于数字孪生的机械设备健康状态监控
Figure 5. Health status monitoring of mechanical equipment based on digital twin
图 6 数据驱动的机械设备故障检测流程
Figure 6. Data-driven mechanical equipment fault detection process
图 11 实时数据驱动的机械臂异常检测
Figure 11. Real-time data-driven abnormal detection of robotic arm
图 12 各性能参数对机械臂异常的影响程度
Figure 12. Influence degree of each performance parameter on mechanical arm abnormality
表 1 各性能参数指标统计表
Table 1. Statistical table of each performance parameter index
变量 最大值 最小值 平均值 伺服电机温度/℃ 79 16 49 电机响应时间/ms 20 6 10 
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表 2 增加扰动后实时采集到的机械臂性能参数
Table 2. Performance parameters of robotic arm collected in real time after addition of disturbance
机械臂性能参数 参数值 关节1处伺服电机温度/℃ 80 关节1处伺服电机响应时间/ms 17 关节2处伺服电机温度/℃ 40 关节2处伺服电机响应时间/ms 19 关节3处伺服电机温度/℃ 30 关节3处伺服电机响应时间/ms 18 关节4处伺服电机温度/℃ 48 关节4处伺服电机响应时间/ms 16 关节5处伺服电机温度/℃ 36 关节5处伺服电机响应时间/ms 14 关节6处伺服电机温度/℃ 45 关节6处伺服电机响应时间/ms 17
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