With the ever-increasing complexity and importance of industrial systems, diagnosis techniques allowing to detect, locate, and identify any abnormalities in the system as early as possible have attracted a lot of attention over the past years. In this article, we present a diagnosis method for nonlinear dynamical systems, called sparse recovery diagnosis, based on a dynamical algorithm that estimates a sparse fault vector from few system measurements. The term sparse means that many faults can be considered, but only few of them can occur simultaneously. To illustrate the performances of this diagnosis method, we apply it to a gear power transmission. This dynamical system is among the most important mechanical components in industrial systems. The gear power transmission model considered in this article is composed by a two-stage gear for which we take into account the torsional effect of the gears. Different sensor and mechanical faults perturbing its operating mode will be modeled and detected by the sparse recovery diagnosis method.

随着工业系统的复杂性和重要性日益增加，允许尽早检测，定位和识别系统中任何异常的诊断技术在过去几年中引起了很多关注。在本文中，我们提出了一种非线性动力学系统的诊断方法，称为稀疏恢复诊断，该方法基于一种动态算法，该算法可以从很少的系统测量值中估计出一个稀疏故障向量。术语“稀疏”意味着可以考虑许多故障，但是只有少数故障可以同时发生。为了说明这种诊断方法的性能，我们将其应用于齿轮传动。该动力系统是工业系统中最重要的机械部件之一。本文考虑的齿轮动力传递模型由两级齿轮组成，为此我们考虑了齿轮的扭转效应。将通过稀疏恢复诊断方法对困扰其工作模式的不同传感器和机械故障进行建模和检测。