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首页> 外文会议>Conference on component and systems diagnostics, prognosis and health management >Sequential Monte Carlo filtering vs. the IMM estimator for fault detection and isolation in nonlinear systems
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Sequential Monte Carlo filtering vs. the IMM estimator for fault detection and isolation in nonlinear systems

机译：序贯蒙特卡罗滤波与非线性系统故障检测与隔离的IMM估计

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In this paper we present and compare different fault diagnosis algorithms using space space models for nonlinear dynamic systems. Most fault diagnosis and isolation algorithms for dynamic systems, which can be modeled using a set of state space equations, have relied on the system being linear and the noise and disturbances being Gaussian. In such cases, optimal filtering ideas based on Kalman filtering are utilized in estimation followed by a residual analysis, for which whiteness tests are typically carried out. Linearized approximations (e.g., Extended Kalman filters) have been used in the nonlinear dynamic systems case. However, linearization techniques, being approximate, tend to suffer from poor detection or high false alarm rates. In this paper, we use the sequential Monte Carlo filtering approach where the complete posterior distribution of the estimates are represented through samples or particles as opposed to the mean and covariance of an approximated Gaussian distribution. The particle filter is combined with the innovation-based fault detection techniques to develop a fault detection and isolation scheme. The advantage of particle filters is that they are capable of handling any functional nonlinearity and system or measurement noise of any distribution. An improvement on using a single Extended Kalman filter matched to a particular model is to use the the Interacting Multiple Model estimator, which consists of a number of EKFs running in parallel. Such a multiple model estimator can handle the abrupt changes in the system dynamics, which is essential for fault diagnosis. Here, we compare the fault detection performances of these algorithms on different nonlinear systems.
机译：在本文中，我们使用非线性动态系统的空间模型来展示和比较不同的故障诊断算法。可以使用一组状态空间方程式建模的动态系统的大多数故障诊断和隔离算法依赖于系统线性和高斯的噪声和干扰。在这种情况下，基于卡尔曼滤波的最佳滤波思路在估计中利用，然后进行残余分析，通常进行白度测试。在非线性动态系统外壳中使用了线性化近似（例如，扩展卡尔曼滤波器）。然而，近似的线性化技术倾向于遭受差的检测或高误报率。在本文中，我们使用顺序蒙特卡罗滤波方法，其中估计的完整后部分布通过样品或粒子表示，而不是近似高斯分布的平均值和协方差。粒子滤波器与基于创新的故障检测技术相结合，以开发故障检测和隔离方案。颗粒滤波器的优点是它们能够处理任何功能性的非线性和系统或测量噪声。使用与特定模型匹配的单个扩展卡尔曼滤波器的改进是使用相互作用的多模型估计器，该估计器由许多并行运行的EKF组成。这种多模型估计器可以处理系统动态的突然变化，这对于故障诊断至关重要。在这里，我们将这些算法的故障检测性能进行比较在不同的非线性系统上。

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