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首页> 外文期刊>Automatic Control, IEEE Transactions on >Maximum Likelihood Failure Diagnosis in Finite State Machines Under Unreliable Observations
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Maximum Likelihood Failure Diagnosis in Finite State Machines Under Unreliable Observations

机译：不确定状态下有限状态机的最大似然故障诊断

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摘要

In this paper, we develop a probabilistic methodology for failure diagnosis in finite state machines based on a sequence of unreliable observations. Given prior knowledge of the input probability distribution but without actual knowledge of the applied input sequence, the core problem we consider is to choose from a pool of known, deterministic finite state machines (FSMs) the one that most likely matches the given sequence of observations. The problem becomes challenging because of sensor failures which may corrupt the observed sequence by inserting, deleting, and transposing symbols with certain probabilities (that are assumed known). We propose an efficient recursive algorithm for obtaining the most likely underlying FSM, given the possibly erroneous observed sequence. The proposed algorithm essentially allows us to perform online maximum likelihood failure diagnosis and is applicable to more general settings where one is required to choose the most likely underlying hidden Markov model (HMM) based on a sequence of observations that may get corrupted with known probabilities. The algorithm generalizes existing recursive algorithms for likelihood calculation in HMMs by allowing loops in the associated trellis diagram. We illustrate the proposed methodology using an example of diagnosis in the context of communication protocols.
机译：在本文中，我们基于一系列不可靠的观察结果，开发了一种在有限状态机中进行故障诊断的概率方法。给定输入概率分布的先验知识，但没有实际了解所应用的输入序列，我们考虑的核心问题是从一组已知的确定性有限状态机（FSM）中选择最可能与给定观测序列相匹配的一个。由于传感器故障，该问题变得具有挑战性，因为传感器故障可能会通过插入，删除和转置具有某些概率（假定为已知）的符号来破坏观察到的序列。给定可能错误的观察序列，我们提出了一种有效的递归算法，用于获得最可能的基础FSM。所提出的算法实质上使我们能够执行在线最大似然性故障诊断，并且适用于需要根据一系列可能因已知概率损坏的观察结果选择最可能的基础隐马尔可夫模型（HMM）的更一般的设置。该算法通过允许相关网格图中的循环，概括了现有递归算法，用于HMM中的似然计算。我们使用通信协议中的诊断示例说明了所提出的方法。

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