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Hidden Markov Models as a Support for Diagnosis: Formalization of the Problem and Synthesis of the Solution

机译：隐藏的马尔可夫模型作为对诊断的支持：解决问题的正式化和解决方案

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In modern information infrastructures, diagnosis must be able to assess the status or the extent of the damage of individual components. Traditional one-shot diagnosis is not adequate, but streams of data on component behavior need to be collected and filtered over time as done by some existing heuristics. This paper proposes instead a general framework and a formalism to model such over-time diagnosis scenarios, and to find appropriate solutions. As such, it is very beneficial to system designers to support design choices. Taking advantage of the characteristics of the hidden Markov models formalism, widely used in pattern recognition, the paper proposes a formalization of the diagnosis process, addressing the complete chain constituted by monitored component, deviation detection and state diagnosis. Hidden Markov models are well suited to represent problems where the internal state of a certain entity is not known and can only be inferred from external observations of what this entity emits. Such over-time diagnosis is a first class representative of this category of problems. The accuracy of diagnosis carried out through the proposed formalization is then discussed, as well as how to concretely use it to perform state diagnosis and allow direct comparison of alternative solutions.
机译：在现代信息基础设施中，诊断必须能够评估各组分损坏的状态或程度。传统的单次诊断不足，但需要收集组件行为的数据流，并随着某些现有启发式完成的时间而过滤。本文提出了一般框架和一种形式主义来模拟这种过度诊断方案，并找到适当的解决方案。因此，对系统设计人员来支持设计选择是非常有益的。利用隐马尔可夫模型的特点形式主义，广泛应用于模式识别，本文提出了诊断过程的形式化，解决了由监测组分，偏差检测和状态诊断构成的完整链。隐藏的马尔可夫模型非常适合代表某些实体的内部状态未知的问题，并且只能从本实体发出的外部观察推断出来。这种过时诊断是一类代表这类问题的第一类。然后讨论了通过拟议的形式化进行的诊断的准确性，以及如何具体使用它来执行状态诊断并允许直接比较替代解决方案。

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