论文标题
隐藏的马尔可夫模型及其预测故障事件的应用
Hidden Markov Models and their Application for Predicting Failure Events
论文作者
论文摘要
我们展示了如何使用Markov混合成员资格模型（MMMM）来预测资产的退化。我们对单个资产的退化路径进行建模，以预测总体失败率。我们使用指数家族中分布的分层混合物，而不是每个隐藏状态的单独分布。在我们的方法中，状态的观察分布是在所有州共享一组（简单）分布的有限混合物分布。使用绑定的观察分布提供了几个优点。该混合物是通常非常稀疏的问题的正则化，并且由于要找到的分布较少，因此减少了学习算法的计算工作。使用共享的混合物可以在马尔可夫州之间共享统计强度，从而转移学习。我们通过将MMMM与可观察到的Markov决策过程（POMDP）相结合，以动态优化何时以及如何维护资产，通过将MMMM与部分可观察到的Markov决策过程（POMDP）相结合来确定单个资产的权衡。
We show how Markov mixed membership models (MMMM) can be used to predict the degradation of assets. We model the degradation path of individual assets, to predict overall failure rates. Instead of a separate distribution for each hidden state, we use hierarchical mixtures of distributions in the exponential family. In our approach the observation distribution of the states is a finite mixture distribution of a small set of (simpler) distributions shared across all states. Using tied-mixture observation distributions offers several advantages. The mixtures act as a regularization for typically very sparse problems, and they reduce the computational effort for the learning algorithm since there are fewer distributions to be found. Using shared mixtures enables sharing of statistical strength between the Markov states and thus transfer learning. We determine for individual assets the trade-off between the risk of failure and extended operating hours by combining a MMMM with a partially observable Markov decision process (POMDP) to dynamically optimize the policy for when and how to maintain the asset.
