基于ARM的旋转机械无线监测智能数据采集平台设计

发布时间：2018-06-06 16:01

本文选题：旋转机械 + 振动监测　；参考：《北京化工大学》2012年硕士论文

【摘要】：本文来源于国家自然科学基金项目—机械故障无线传感网络监测与智能诊断方法研究（51075023）。旋转机械设备是现今工业生产系统的重要组成部分，其运行状态对生产系统的稳定性及安全性有着直接影响，设计高效的旋转机械振动监测装置对于防止故障发生有着重大的实际意义。目前针对旋转机械的监测方案主要为基于专家系统的在线监测方式和基于便携式终端的人工巡检方式，，前者的振动信号采集点数据处理能力有限，主要依赖于大数据量的振动信号上传，因此对数据传输带宽及相应的线路配置要求较高，而后者受制于专业人员的检查方式且难以实现不间断监测。本文自主设计的基于ARM的嵌入式RTAI+Linux智能数据采集平台弥补了现有监测方式的不足，通过加载监测诊断算法能够准确地对采集的振动信号进行状态识别和初步故障诊断，从而提高了信号采集端的数据处理和识别能力，并且可加载无线数据传输模块将监测结果或故障数据通过无线网络传输到上位机，从而弥补了人工巡查和有线传输的不足。本文自主设计的嵌入式RTAI+Linux智能数据采集平台主要由智能双模传感器、信号预处理、数据处理和传输等部分组成。本文提出了一种自适应选择较优振动信号的双模传感器设计方案，并完成了其硬件设计和研制，该双模传感器由两种不同测量范围和精度的加速度传感器组成，采集平台能够在满足量程的基础上动态选择较高测量精度的信号；完成了以ARM为核心的数据采集平台硬件设计，该平台采用嵌入式RTAI+Linux双内核结构，此模式下设计的底层驱动程序和应用程序能够更实时高效地对硬件请求和中断进行响应，从而提高底层数据通信和处理效率；智能数据采集平台能够通过加载的监测诊断算法对振动信号进行状态特征提取和异常诊断，依据设备状态分别将特征参数或振动波形数据传至监测系统数据服务器。最后将智能数据采集平台用于振动实验台滚动轴承监测，实验证明其能够实现振动信号的采集、信号的特征参数提取以及轴承状态的简易识别，并能够将特征参数和故障波形数据传至服务器。该平台的构建为实现整个设备的无线监测系统提供了技术基础。
[Abstract]:This paper comes from the project of National Natural Science Foundation of China-Research on Monitoring and Intelligent diagnosis method of Mechanical Fault Wireless Sensor Network. Rotating machinery is an important part of industrial production system. Its running state has a direct impact on the stability and safety of the production system. It is of great practical significance to design an efficient vibration monitoring device for rotating machinery to prevent the occurrence of faults. At present, the monitoring schemes for rotating machinery are mainly based on the online monitoring mode based on expert system and the manual inspection mode based on portable terminal. The former has limited data processing ability of vibration signal acquisition points. It mainly depends on the vibration signal upload of large amount of data, so the data transmission bandwidth and the corresponding line configuration are very high, and the latter is restricted by the inspection way of the professionals and it is difficult to realize the continuous monitoring. The embedded RTAI Linux intelligent data acquisition platform based on arm is designed in this paper to make up for the deficiency of the existing monitoring methods. By loading the monitoring and diagnosis algorithm, we can accurately identify the state of the collected vibration signal and diagnose the initial fault. Thus, the data processing and recognition ability of the signal acquisition terminal is improved, and the wireless data transmission module can be loaded to transmit the monitoring results or fault data to the upper computer through the wireless network. The embedded RTAI Linux intelligent data acquisition platform is mainly composed of intelligent dual-mode sensor, signal preprocessing, data processing and transmission. In this paper, a design scheme of a dual-mode sensor which adaptively selects a better vibration signal is proposed, and its hardware design and development are completed. The dual-mode sensor is composed of two accelerometers with different measuring range and precision. The acquisition platform can dynamically select the signal with high measurement precision on the basis of satisfying the range, and complete the hardware design of the data acquisition platform with arm as the core. The platform adopts the embedded RTAI Linux dual kernel structure. The underlying drivers and applications designed in this mode can respond to hardware requests and interrupts more efficiently in real time so as to improve the communication and processing efficiency of the underlying data. The intelligent data acquisition platform can extract the state feature and diagnose the abnormal of vibration signal through the loaded monitoring and diagnosis algorithm. According to the state of the equipment, the characteristic parameters or the vibration waveform data are transferred to the data server of the monitoring system respectively. Finally, the intelligent data acquisition platform is used in the rolling bearing monitoring of the vibration test bench, which is proved to be able to collect the vibration signals. The feature parameters of the signal are extracted and the bearing state can be easily identified, and the characteristic parameters and fault waveform data can be transmitted to the server. The construction of the platform provides the technical foundation for the wireless monitoring system of the whole equipment.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH165.3;TP274

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