Monitoring of industrial processes is a critical capability in industry and in government to ensure reliability of production cycles, quick emergency response, and national security. Process monitoring allows users to gauge the involvement of an organization in an industrial process or predict the degradation or aging of machine parts in processes taking place at a remote location. Similar to many data science applications, we usually only have access to limited raw data, such as satellite imagery, short video clips, some event logs, and signatures captured by a small set of sensors. To combat data scarcity, we leverage the knowledge of subject matter experts (SMEs) who are familiar with the process. Various process mining techniques have been developed for this type of analysis; typically such approaches combine theoretical process models built based on domain expert insights with ad-hoc integration of available pieces of raw data. Here, we introduce a novel mathematically sound method that integrates theoretical process models (as proposed by SMEs) with interrelated minimal Hidden Markov Models (HMM), built via non-negative tensor factorization and discrete model simulations. Our method consolidates: (a) Theoretical process models development, (b) Discrete model simulations (c) HMM, (d) Joint Non-negative Matrix Factorization (NMF) and Non-negative Tensor Factorization (NTF), and (e) Custom model selection. To demonstrate our methodology and its abilities, we apply it on simple synthetic and real world process models.
翻译:对工业过程的监测是工业和政府的关键能力,以确保生产周期、快速应急反应和国家安全的可靠性。过程监测使用户能够衡量一个组织参与工业过程的情况,或预测在偏远地点进行的工艺过程中机器部件的退化或老化。与许多数据科学应用类似,我们通常只能获得有限的原始数据,如卫星图像、短视频剪辑、一些事件日志和由一小批传感器获取的签名等。为了消除数据稀缺,我们利用熟悉该过程的主题专家的知识。为这种分析开发了各种过程采矿技术;这类方法通常将基于领域专家见解建立的理论过程模型与现有原始数据片段的自动整合结合起来。在这里,我们采用了一种新的、数学上健全的方法,将理论过程模型(如中小企业提议的那样)与相关的最低隐蔽的Markov模型(HMMM)结合起来,这些模型是通过非内建模型因子因子化模型和离子模型模拟而建立的。我们的方法巩固了下述方法:(a) 理论过程模型的开发,(b) Discrete N模型模拟(HMMMM) 和MIT(N(MIT) 联合模拟(C) (HMMMM) 和MIT(M) (M) (M) (MIT(M) (M) (MIT) (MIT) (MIT) (M) (M) (M) (M) (M) (M) (M) (M) (N(M) (M) (M) (M) (M) (N(M) (M) (M) (N(M) (M) (M) (N(N(M) (N(M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (M) (N(M) (M) (N(N(N) (N) (N(N(M) (M) (M) (N(M) (N(N(N(N(N))) (N) (N) (N) (N) (N) (M) (N) (N(N(N) (N(N(N(