Clustering and Automatic Labelling Within Time Series of Categorical Observations—With an Application to Marine Log Messages
提出一种离线方法，将时间序列日志消息分割成随机长度的离散段并聚类成有限状态，每个状态对应系统特定操作或故障模式，通过贝叶斯层次模型和可逆跳转MCMC算法进行推断，适用于复杂工业系统的故障识别。
Abstract System logs or log files containing textual messages with associated time stamps are generated by many technologies and systems. The clustering technique proposed in this paper provides a tool to discover and identify patterns or macrolevel events in this data. The motivating application is logs generated by frequency converters in the propulsion system on a ship, while the general setting is fault identification and classification in complex industrial systems. The paper introduces an offline approach for dividing a time series of log messages into a series of discrete segments of random lengths. These segments are clustered into a limited set of states. A state is assumed to correspond to a specific operation or condition of the system, and can be a fault mode or a normal operation. Each of the states can be associated with a specific, limited set of messages, where messages appear in a random or semi-structured order within the segments. These structures are in general not defined a priori. We propose a Bayesian hierarchical model where the states are characterised both by the temporal frequency and the type of messages within each segment. An algorithm for inference based on reversible jump MCMC is proposed. The performance of the method is assessed by both simulations and operational data.