基于分层统计建模的工业资产异常检测

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
M. Dhada, M. Girolami, A. Parlikad
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引用次数: 8

摘要

资产状态数据异常检测是工业资产可靠运行的关键。但是统计异常分类器需要一定数量的正常操作训练数据才能达到可接受的准确率。在资产业务的初期,往往没有必要的训练数据。在本文中,我们使用了一个系统地识别相似资产的资产群的层次模型来解决这个问题,并在相似资产的集群中实现协作学习。类似资产的一般行为使用更高层次的模型来表示,从这些模型中采样参数来描述单个资产操作。分层模型使群体中的个体(由统计上一致的子群体组成)能够相互协作学习。与独立建模或整个船队通用模型相比,使用分层模型获得的结果显示,对于数据量较少的资产,异常检测有显着改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Anomaly detection in a fleet of industrial assets with hierarchical statistical modeling
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.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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