...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft
【24h】

Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft

机译：基于曲轴角速度变化的大型柴油机基于模型的诊断

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This work aims at monitoring large diesel engines by analyzing the crankshaft angular speed variations. It focuses on a powerful 20-cylinder diesel engine with crankshaft natural frequencies within the operating speed range. First, the angular speed variations are modeled at the crankshaft free end. This includes modeling both the crankshaft dynamical behavior and the excitation torques. As the engine is very large, the first crankshaft torsional modes are in the low frequency range. A model with the assumption of a flexible crankshaft is required. The excitation torques depend on the in-cylinder pressure curve. The latter is modeled with a phenomenological model. Mechanical and combustion parameters of the model are optimized with the help of actual data. Then, an automated diagnosis based on an artificially intelligent system is proposed. Neural networks are used for pattern recognition of the angular speed waveforms in normal and faulty conditions. Reference patterns required in the training phase are computed with the model, calibrated using a small number of actual measurements. Promising results are obtained. An experimental fuel leakage fault is successfully diagnosed, including detection and localization of the faulty cylinder, as well as the approximation of the fault severity.
机译：这项工作旨在通过分析曲轴角速度变化来监视大型柴油发动机。它专注于功能强大的20缸柴油发动机，其曲轴固有频率在运行速度范围内。首先，在曲轴自由端模拟角速度变化。这包括对曲轴动力学行为和激励扭矩进行建模。由于发动机非常大，因此第一曲轴扭转模式处于低频范围内。需要一个带有挠性曲轴的模型。励磁扭矩取决于缸内压力曲线。后者是用现象学模型建模的。该模型的机械和燃烧参数在实际数据的帮助下进行了优化。然后，提出了一种基于人工智能系统的自动化诊断方法。神经网络用于正常和故障情况下角速度波形的模式识别。使用该模型计算训练阶段所需的参考模式，并使用少量实际测量值对其进行校准。获得了有希望的结果。已成功诊断出实验性燃油泄漏故障，包括对故障气缸的检测和定位以及故障严重程度的估算。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2010年第5期|1529-1541|共13页
  • 作者

    M. Desbazeille; rnR.B. Randall; rnF. Guillet; rnM. El Badaoui; rnC. Hoisnard;

  • 作者单位

    Universite de Lyon, F-42023, Saint Etienne, France Universite de Saint-Etienne, Jean Monnet, F-42000, Saint-Etienne, France LASPI, F-42334, IUT de Roanne, France;

    rnSchool of Mechanical and Manufacturing Engineering, The University of New South Wales Sydney, 2052, Australia;

    rnUniversite de Lyon, F-42023, Saint Etienne, France Universite de Saint-Etienne, Jean Monnet, F-42000, Saint-Etienne, France LASPI, F-42334, IUT de Roanne, France;

    rnUniversite de Lyon, F-42023, Saint Etienne, France Universite de Saint-Etienne, Jean Monnet, F-42000, Saint-Etienne, France LASPI, F-42334, IUT de Roanne, France;

    rnEDF R&D Chatou, 78401 Chatou Cedex, France;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    large diesel engines; flexible crankshaft; angular speed variations; diagnosis; pattern recognition;

    机译：大型柴油机;挠性曲轴角速度变化;诊断;模式识别;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号