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A technique for the vibration signal analysis in vehicle diagnostics

机译:车辆诊断中的振动信号分析技术

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摘要

The method of utilising signals of vibration acceleration in the on-line and off-line diagnostics of mechanical defects of internal combustion engines is presented in the paper. The monitored vibration signals of the spark ignition (SI) engine in various maintenance states of the valve system were investigated. The suggested technique is based on mathematical methods of the lower triangular-orthogonal (LQ) factorisation and the singular value decomposition (SVD) of observation subspaces computed on a vibration time series after their angular resampling without any transformations in the frequency domain. The applied algorithm of data processing filters excessive information and allows the selection of diagnostic features (essential from the maintenance point of view) and generates the empirical model and matrix residuals assessed in the no-fault state as being 'zero'. Then, statistical feature vectors, for which the averaged successive singular values of the residuals of the observation subspaces of the vibration signals were assumed as components, were analysed. As a result of this procedure the vectors of lower dimensions reduced to components, allowing the classification of observations within all defined classes, were obtained. On the basis of these vectors a scalar measure - sensitive to the kind of defect - was proposed and verified.
机译:本文提出了一种在内燃机的机械缺陷在线和离线诊断中利用振动加速度信号的方法。研究了在气门系统各种维护状态下火花点火(SI)发动机的监测振动信号。所建议的技术基于下三角正交(LQ)分解和观测子空间的奇异值分解(SVD)的数学方法,这些观测子空间是在角度重采样后在振动时间序列上计算出来的,而它们在频域中没有任何变换。所应用的数据处理算法可以过滤掉过多的信息,并可以选择诊断功能(从维护的角度来看是必不可少的),并生成在无故障状态下评估为“零”的经验模型和矩阵残差。然后,分析统计特征向量,将其作为振动信号观测子空间残差的平均连续奇异值作为分量。作为该过程的结果,获得了维数减少为分量的向量,从而可以在所有定义的类中对观察结果进行分类。基于这些向量,提出并验证了对缺陷类型敏感的标量度量。

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