Abstract
Abstract
Anomaly detection in asset condition data is critical for reliable industrial asset operations. But statistical anomaly classifiers require certain amount of normal operations training data before acceptable accuracy can be achieved. The necessary training data are often not available in the early periods of assets operations. This problem is addressed in this paper using a hierarchical model for the asset fleet that systematically identifies similar assets, and enables collaborative learning within the clusters of similar assets. The general behavior of the similar assets are represented using higher level models, from which the parameters are sampled describing the individual asset operations. Hierarchical models enable the individuals from a population, comprising of statistically coherent subpopulations, to collaboratively learn from one another. Results obtained with the hierarchical model show a marked improvement in anomaly detection for assets having low amount of data, compared to independent modeling or having a model common to the entire fleet.
Publisher
Cambridge University Press (CUP)
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference38 articles.
1. Smart Factories
2. Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters
3. Economou, T , Kapelan, Z and Bailey, T (2007) An aggregated hierarchical Bayesian model for the prediction of pipe failures. In Proceedings of the Combined International Conference of Computing and Control for the Water Industry, CCWI2007 and Sustainable Urban Water Management, SUWM2007.
4. Bayesian Data Analysis
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献