预知维修

  • 网络Predictive Maintenance
预知维修预知维修
  1. 基于BP网络的装备预知维修

    Predictive Maintenance of Equipment Based on BP Neural Network

  2. 本课题就是在充分考虑大中型企业设备管理的实际情况之后,决定设计&开发基于C/S结构的分布式故障诊断与预知维修系统。

    So the distributed fault diagnosis and predictive maintenance system based on C / S structure is exploited after considering the equipment management of large enterprises .

  3. 基于ANN的工程机械预知维修专家系统研究

    Research on the Expert System of Predictive Maintenance of Engineering Machine based on the Artificial Neural Network

  4. 具有高度非线性和很强的自学习能力的BP神经网络使预知维修成为可能,但由于BP算法存在收敛速度慢、易出现局部极小值等缺陷。

    With highly nonlinear and a self-learning ability , BP neural network makes it possible , and however , the BP algorithm has a very slow convergence speed and gets into a local minimum easily .

  5. 故障诊断与预知维修系统中的数据库设计

    Database Design of the System of Fault Diagnose and Predicting Repair

  6. 设备状态监测、故障诊断与预知维修技术的应用

    Application of equipment status monitoring , breakdown diagnosis and prevision repair

  7. ISPMAT&适用于城铁列车的预知维修智能系统

    Ispmat : intelligent system for predictive maintenance applied to trains

  8. 机电设备预知维修与故障诊断系统设计

    Design of Predictive Maintenance and Fault Diagnosis for Mechanical and Electrical Equipments

  9. 基于预知维修的网络化设备状态监测系统的实现

    Research on the Network Condition Monitor System based on the Predictive Maintenance

  10. 预知维修在海洋石油设备维修中的应用

    The Application of Prefigure Service of Mechanical Equipment in China Offshore Oil

  11. 滚动轴承故障诊断与预知维修数据库系统研制

    Research on fault diagnosis and predictive maintenance of roller bearings

  12. 基于油液分析状态监测的预知维修方法

    Foreknowledge maintenance methods based on oil analysis and state monitor

  13. 预知维修技术在发动机机加工设备管理中的应用使机动化用机动车辆装备

    Application of Predictive Maintenance Technology in Engine Machining Equipment Management

  14. 基于局域波法的车用柴油机预知维修研究

    Study on Predicting Maintenance of Vehicle Diesel Engine Based on Local-wave Method

  15. 火炮预知维修专家系统中的预测推理机制

    Forecast Reasoning Mechanism in Predictive Expert System for Artillery

  16. 工程机械预知维修的油样状态监测法

    Oil sample quality monitoring in construction machinery predictive maintenance

  17. 港口流动机械预知维修系统

    Design of predicting maintenance system for port mobile machinery

  18. 基于润滑油监测技术的铁路大型养路机械预知维修

    Predictive Maintenance to Large Track Maintenance Machinery in Railway Based on Oil Monitoring

  19. 阐述了铁路大型养路机械设备实施预知维修的必要性和先进性。

    The advantage and necessity in large track maintenance machinery predictive maintenance was described .

  20. 介绍了北京铁路局机械段以润滑油监测技术为基础,开展预知维修工作的具体情况。

    Some test examples based on oil monitoring were introduced in Beijing Railway Administration .

  21. 成功实例证明了预知维修的可行性及实效性。

    From these successful applications , it proves that predictive maintenance is feasible and practicable .

  22. 机械故障诊断对于设备的安全、连续运行和预知维修至关重要。

    The fault diagnosis is essential to equipment safety , continuous operation and predictive maintenance .

  23. 机械设备计算机辅助简易预知维修系统

    Computer Aided Predicting Maintenance System for Machine

  24. 针对这一问题,引进了状态维修的概念。状态维修,亦称预知维修,是一种以设备技术状态为依据的预防性维修。

    According to this problem , we introduce into the notion of Condition Based Maintenance .

  25. 港口叉车变速箱故障诊断与预知维修系统的研究

    Study on Fault Diagnosis and Predicting Maintain to Gearbox of Forklift Truck in the port

  26. 利用状态监测技术,逐步实现从计划维修向预知维修的过渡。

    Gradully realize the change from planing maintenance to predictive maintenance by using state detecting technique .

  27. 远程故障诊断技术在设备预知维修系统中的应用研究

    Study on the Application of Remote Fault Diagnosis Technologies in Machine Conditioning Based Monitoring and Maintenance System

  28. 机械设备状态监测及预知维修技术是机械设备现代管理的主要内容。

    Mechanical device condition monitoring and preventative maintenance are the main content of modern management of machinery .

  29. 油样状态监测方法作为工程机械预知维修的一种重要方法已得到越来越多的应用。

    Oil sample quality monitoring as an important means of construction machinery predictive maintenance has found increasingly wide application .

  30. 阐述了车用柴油机预知维修的原理及意义和局域波法原理,利用此方法对柴油机缸盖表面振动信号进行分析,提取出能够表征其内部状态信息的特征参数。

    The principle of vehicle diesel engine predicting maintenance was discussed and feature parameters were obtained using local wave method .