• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A New Fault Diagnosis Method for a Diesel Engine Based on an Optimized Vibration Mel Frequency under Multiple Operation Conditions
【2h】

A New Fault Diagnosis Method for a Diesel Engine Based on an Optimized Vibration Mel Frequency under Multiple Operation Conditions

机译：基于最优振动梅尔频率的多工况柴油机故障诊断新方法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The diesel engine has been a significant component of large-scale mechanical systems for the intelligent manufacturing industry. Because of its complex structure and poor working environment, it has trouble effectively acquiring the representative fault features. Further, fault diagnosis of the diesel engine faces great challenges. This paper presents a new fault diagnosis method for the detection of diesel engine faults under multiple operation conditions instead of conventional methods confined to a single condition. First, an adaptive correlation threshold process is designed as a preprocessing unit to enhance data quality by weakening non-impact region characteristics. Next, a feature extraction method for sound signals based on the Mel frequency cepstrum (MFC) is improved and introduced into the machinery fault diagnosis. Then, the combination of the improved feature and vibrational mode decomposition (VMD) is proposed to incorporate VMD into an effective adaptive decomposition of non-stationary signals to combine it with an excellent feature representation of the vibration signal. Finally, the vector quantization algorithm is adopted to reduce the feature dimensions and generate codebook model bases, which trains the K-Nearest Neighbor classifiers. Five comparative methods were carried out, and the experimental results show that the proposed method offers a good effect of the common valve clearance fault of diesel engines under different conditions.
机译：柴油发动机已经成为智能制造行业大型机械系统的重要组成部分。由于其结构复杂，工作环境恶劣，难以有效地获得代表故障特征。此外，柴油机的故障诊断面临巨大挑战。本文提出了一种新的故障诊断方法，用于检测多种工况下的柴油机故障，而不是局限于单一条件的常规方法。首先，将自适应相关阈值过程设计为预处理单元，以通过削弱非影响区域特性来提高数据质量。接下来，改进了基于梅尔频率倒谱（MFC）的声音信号特征提取方法，并将其引入机械故障诊断中。然后，提出了改进特征和振动模式分解（VMD）的组合，以将VMD合并到非平稳信号的有效自适应分解中，以将其与振动信号的出色特征表示相结合。最后，采用矢量量化算法来减少特征量并生成码本模型库，从而训练K最近邻分类器。进行了五种比较方法，实验结果表明，该方法对不同条件下柴油机常见的气门间隙故障具有良好的效果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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