Anomaly detection in a fleet of industrial assets with hierarchical statistical modeling
Anomaly detection in a fleet of industrial assets with hierarchical statistical modeling
复制标题
通过分层统计模型检测工业资产中的异常情况
DOI：
10.1017/dce.2020.19
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
A. Parlikad
中科院分区：
文献类型：
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作者：
M. Dhada;M. Girolami;A. Parlikad
摘要资产状态数据中的异常检测是工业资产可靠运行的关键。但统计异常分类器需要一定量的正常操作训练数据，才能达到可接受的精度。在资产业务的早期阶段，往往没有必要的培训数据。本文使用资产编队的分层模型来解决这个问题，该模型系统地识别相似资产，并允许在相似资产集群内进行协作学习。相似资产的一般行为使用更高级别的模型来表示，从该更高级别的模型中采样描述单个资产操作的参数。分层模型使群体中的个体能够相互协作地学习，该群体由统计上一致的子群体组成。使用分层模型获得的结果显示，与独立建模或具有对整个舰队通用的模型相比，对于具有低数据量的资产的异常检测具有显著的改进。
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.
DOI：
10.1016/j.future.2018.02.011
发表时间：
2019-03
期刊：
Future Gener. Comput. Syst.
影响因子：
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作者：
A. S. Palau;Zhenglin Liang;Daniel Lütgehetmann;A. Parlikad
通讯作者：
A. S. Palau;Zhenglin Liang;Daniel Lütgehetmann;A. Parlikad