基于边缘计算的滚动轴承智能监测系统研究

Research on Intelligent Monitoring System of Rolling Bearing Based on Edge Computing

工业大数据环境下,受云中心计算能力和数据传输带宽的制约,降低了滚动轴承云平台在线检测系统的数据处理效率和实时性。针对这一问题,提出一种基于边缘计算的滚动轴承智能监测系统。该系统采用分层递进模式,将训练测试好的连续隐马尔科夫模型布置在边缘层,对滚动轴承的振动信号提取时/频域特征,用随机森林算法进行特征重要性评估,建立敏感特征集并输入模型,在边缘层进行状态监测和初步故障诊断。通过上传故障数据至云层,进行包络谱分析,做出最终判断和维修安排。通过滚动轴承实测信号对该系统进行了分析验证,结果表明该系统具有较高的稳定性和识别准确率,具备满足实时性要求的性能,提高监测效率。

Industrial big data is taking massive strides forward,but the data processing efficiency and real-time performance of the online rolling bearing inspection system on the cloud platform are poor,which are restricted by the computing power and data transmission bandwidth of cloud center.An intelligent rolling bearing monitoring system is proposed based on edge computing.The system adopts a hierarchical progressive mode.Firstly,big data was trained and tested in a continuous hidden Markov model which is arranged on the edge layer.It extracts time-frequency features of vibration signals of the rolling bearing,evaluates the importance of these features by random forest algorithm,establishes the sensitive feature sets and inputs them into the continuous hidden Markov model.All of this is to finish condition monitoring and preliminary fault diagnosis on the edge layer.Then,the primary diagnosis is uploaded to the cloud platform,and the final judgment and maintenance arrangement are made using envelope spectrum analysis.According to the analysis on the experiment signals of rolling bearings,it is verified that the system has high stability and recognition accuracy to meet the real-time requirements with high monitoring efficiency.